We use an online experiment with a real work task to study whether workers change their behavior when they know AI will be used to judge their work instead of humans. We find that individuals produce a higher quantity of output when they are assigned an AI evaluator. However, controlling for quantity, the quality of their output is lower, regardless of whether quality is measured using humans or LLM grades. We also find that workers are more likely to use external tools, including LLMs, when they know AI is used to judge their work instead of humans. However, the increase in external tool use does not appear to explain the differences in quantity or quality across treatments.

We study a dynamic model of a non-life insurance portfolio. The foundation of the model is a compound Poisson process that represents the claims side of the insurer. To introduce clusters of claims appearing, e.g. with catastrophic events, this process is time-changed by a Lévy subordinator. The subordinator is chosen so that it evolves, on average, at the same speed as calendar time, creating a trade-off between intensity and severity. We show that such a transformation always has a negative impact on the probability of ruin. Despite the expected total claim amount remaining invariant, it turns out that the probability of ruin as a function of the initial capital falls arbitrarily slowly depending on the choice of the subordinator.

We present a large scale benchmark of modern deep learning architectures for a financial time series prediction and position sizing task, with a primary focus on Sharpe ratio optimization. Evaluating linear models, recurrent networks, transformer based architectures, state space models, and recent sequence representation approaches, we assess out of sample performance on a daily futures dataset spanning commodities, equity indices, bonds, and FX spanning 2010 to 2025. Our evaluation goes beyond average returns and includes statistical significance, downside and tail risk measures, breakeven transaction cost analysis, robustness to random seed selection, and computational efficiency. We find that models explicitly designed to learn rich temporal representations consistently outperform linear benchmarks and generic deep learning models, which often lead the ranking in standard time series benchmarks. Hybrid models such as VSN with LSTM, a combination of Variable Selection Networks (VSN) and LSTMs, achieves the highest overall Sharpe ratio, while VSN with xLSTM and LSTM with PatchTST exhibit superior downside adjusted characteristics. xLSTM demonstrates the largest breakeven transaction cost buffer, indicating improved robustness to trading frictions.

Decarbonizing long-haul freight requires large-scale deployment of high-power charging infrastructure. This paper studies a multi-period charging station location problem that determines where and when to deploy charging capacity for battery-electric heavy-duty vehicles under uncertain future demand and local grid capacity availability. The problem is formulated as a two-stage stochastic mixed-integer program that maximizes covered electric freight flow. Feasible truck routes are generated a priori using a resource-constrained label-setting algorithm that enforces range limitations and driving-break regulations. To solve large-scale instances, an integer L-shaped decomposition method embedded in a branch-and-cut framework and accelerated by a deterministic warm start is implemented. Computational experiments are conducted on a nationwide Norwegian case study based on real candidate locations provided by a charging station operator. The approach solves instances intractable for a monolithic formulation and achieves near-optimal solutions within practical runtimes. For larger networks, the value of the stochastic solution is substantial, highlighting the importance of explicitly modeling uncertainty in long-term infrastructure planning. Optimal investments prioritize major freight corridors in early periods and subsequently reinforce and expand the network. Grid capacity constraints discourage large, concentrated stations and shift deployments toward more distributed layouts. Covered demand increases rapidly at low budget levels but exhibits diminishing returns as the network approaches saturation.

Selective mortality and fertility issues are persistent challenges in estimating the fetal origin effect, with attempts to address these issues being notably scarce. Evidence further suggests that selective mortality is more pronounced in males than in females. This study investigates the causal effects of prenatal exposure to the Great Chinese Famine on educational attainment by addressing gender-specific selection bias. We compare exposed individuals with their unexposed, same-gender siblings, using a famine intensity measure based on county-year level excess death rates. Our findings reveal remarkably similar consequences for both genders: on average, famine exposure increased illiteracy rates by 4 percentage points and decreased years of schooling by 0.3 years for both males and females. These results contribute to our understanding of the long-term impacts of prenatal malnutrition, while accounting for gender-specific selection biases.

The conditional mean risk-sharing (CMRS) rule is an important tool for distributing aggregate losses across individual risks, but its implementation in continuous multivariate models typically requires complicated multidimensional integrals. We develop a framework to compute CMRS allocations from the joint Laplace--Stieltjes transform of the risk vector. The LSTs of the allocation measures $ν_i(B)=\mathbb{E}[X_i\boldsymbol{1}_{\{S\in B\}}]$ are expressed as partial derivatives of the joint LST evaluated on the diagonal $t_1=\cdots=t_n$. When densities exist, this yields one-dimensional Laplace inversions for $f_S$ and $ξ_i$, and hence $h_i(s)=ξ_i(s)/f_S(s)$ on the absolutely continuous part, providing closed-form or semi-analytic solutions for a broad class of distributions. We also develop numerical inversion methods for cases where analytic inversion is unavailable. We introduce an exponential tilting procedure to stabilize numerical inversion in low-probability aggregate events. We provide several examples to illustrate the approach, including in some high-dimensional settings where existing approaches are infeasible.

This paper develops a robust mathematical framework for Constant Function Market Makers (CFMMs) by transitioning from traditional token reserve analyses to a coordinate system defined by price and intrinsic liquidity. We establish a canonical parametrization of the bonding curve that ensures dimensional consistency across diverse trading functions, such as those employed by Uniswap and Balancer, and demonstrate that asset reserves and value functions exhibit a linear dependence on this intrinsic liquidity. This linear structure facilitates a streamlined approach to arbitrage-free pricing, delta hedging, and systematic risk management. By leveraging the Carr-Madan spanning formula, we characterize Impermanent Loss (IL) as a weighted strip of vanilla options, thereby defining a fine-grained implied volatility structure for liquidity profiles. Furthermore, we provide a path-dependent analysis of IL using the last-passage time. Empirical results from Uniswap v3 ETH/USDC pools and Deribit option markets confirm a volatility smile consistent with crypto-asset dynamics, validating the framework's utility in characterizing the risk-neutral fair value of liquidity provision.

This article examines how COVID-19 and the Dobbs decision have impacted abortion services in Louisiana. COVID-19's introduction into an already restrictive landscape of abortion policies intensified the barriers that providers and communities faced, with disproportionate impacts on Black and Hispanic abortion seekers. The 2022 Dobbs decision marked the immediate enactment of Louisiana's abortion ban, resulting in even greater difficulties in accessing abortion services. Concerns raised by Roberts et al. (2021) about the negative effects of clinic closures have only grown since their prescient study.

This paper examines changes in occupational crowding of immigrant women in frontline industries in the United States during the onset of COVID-19, and we contextualize their experiences against the backdrop of broader race-based and gender-based occupational crowding. Building on the occupational crowding hypothesis, which suggests that marginalized workers are crowded in a small number of occupations to prop up wages of socially-privileged workers, we hypothesize that immigrant, Black, and Hispanic workers were shunted into frontline work to prop up the health of others during the pandemic. Our analysis of American Community Survey microdata indicates that immigrant workers, particularly immigrant women, were increasingly crowded in frontline work during the onset of the pandemic. We also find that US-born Black and Hispanic workers disproportionately faced COVID-19 exposure in their work, but were not increasingly crowded into frontline occupations following the onset of the pandemic. The paper also provides a rationale for considering the occupational crowding hypothesis along the dimensions of both wages and occupational health.

How nations shape the scientific frontier matters for technological competition, but standard metrics, including publication counts, citations, and disruption indices, look backward and fail to distinguish between fundamentally different leadership strategies. We develop and validate two forward-looking model-based measures and apply them to tens of millions of articles since 1990. The first embeds research pathways within an evolving hypergraph of concepts and scientists to identify leadership in emerging areas--work that anticipates where the scientific crowd is heading. The second embeds evolving samples of ideas and disciplines drawn upon in past research to identify leadership in surprising new directions as unexpected combinations become routine and science reorganizes around them. China became the global leader in emerging areas roughly a decade ago, well before it led in volume, reflecting a capacity to detect and amplify nascent consensus at scale. The United States and Europe show the opposite profile: declining emergence shares but persistent leadership in prescient work, especially research bridging disciplinary boundaries. These patterns replicate across databases, attribution methods, and strategic domains, including AI, biotechnology, energy, and semiconductors. Nations lead science by reading the landscape or by reshaping it, and the institutional requirements for each strategy lie in tension. The distribution of these strategies promises to shape the global structure of technological leadership for decades.

Social media platforms systematically reward popularity over authenticity, incentivizing users to strategically tailor their expression for attention. In this paper, we introduce (i) popularity as a strategic expression mechanism, distinct from the canonical mechanisms of conformity, learning, persuasion, and (mis)information transmission in social networks, and (ii) a utilitarian framework for measuring user welfare that maps directly to observable platform metrics, filling a critical gap in the social media literature. In the model, agents hold fixed heterogeneous authentic opinions and derive utility gains from the popularity of their own posts -- measured by likes received, and utility gains (losses) from exposure to content that aligns with (diverges from) their authentic opinion. Social media interaction acts as a state-dependent welfare amplifier: light topics generate Pareto improvements, whereas intense topics make everyone worse off in a polarized society (e.g., political debates during elections). Moreover, strategic expression amplifies social media polarization during polarized events while dampening it during unified events (e.g., national celebrations). Consequently, strategic distortions magnify welfare outcomes, expanding aggregate gains in light topics while exacerbating losses in intense, polarized ones. Counterintuitively, strategic agents often face a popularity trap: posting a more popular opinion is individually optimal, yet collective action by similar agents eliminates their authentic opinion from the platform, leaving them worse off than under the authentic-expression benchmark. Homophilic algorithms that match users with preferred content -- widely used by platforms -- discipline popularity-driven behavior, narrowing the popularity trap region and limiting its welfare effects.

This paper investigates the relationship between AI use and worker well-being outcomes such as mental health, job enjoyment, and physical health and safety, using microdata from the OECD AI Surveys across seven countries. The results reveal that AI users are significantly more likely to report improvements across all three outcomes, with effects ranging from 8.9% to 21.3%. However, these benefits vary by generation and gender. Generation Y (1981-1996) shows the strongest gains across all dimensions, while Generation X (1965-1980) reports moderate improvements in mental health and job enjoyment. In contrast, Generation Z (1997-2012) benefits only in job enjoyment. As digital natives already familiar with technology, Gen Z workers may not receive additional gains in mental or physical health from AI, though they still experience increased enjoyment from using it. Baby Boomers (born before 1965) experience limited benefits, as they may not find these tools as engaging or useful. Women report stronger mental health gains, whereas men report greater improvements in physical health. These findings suggest that AI's workplace impact is uneven and shaped by demographic factors, career stage, and the nature of workers' roles.

While Large Language Models have been used to produce interpretable stock forecasts, they mainly focus on analyzing textual reports but not historical price data, also known as Technical Analysis. This task is challenging as it switches between domains: the stock price inputs and outputs lie in the time-series domain, while the reasoning step should be in natural language. In this work, we introduce Verbal Technical Analysis (VTA), a novel framework that combine verbal and latent reasoning to produce stock time-series forecasts that are both accurate and interpretable. To reason over time-series, we convert stock price data into textual annotations and optimize the reasoning trace using an inverse Mean Squared Error (MSE) reward objective. To produce time-series outputs from textual reasoning, we condition the outputs of a time-series backbone model on the reasoning-based attributes. Experiments on stock datasets across U.S., Chinese, and European markets show that VTA achieves state-of-the-art forecasting accuracy, while the reasoning traces also perform well on evaluation by industry experts.

Useful social science theories predict behavior across settings. However, applying a theory to make predictions in new settings is challenging: rarely can it be done without ad hoc modifications to account for setting-specific factors. We argue that AI agents put in simulations of those novel settings offer an alternative for applying theory, requiring minimal or no modifications. We present an approach for building such "general" agents that use theory-grounded natural language instructions, existing empirical data, and knowledge acquired by the underlying AI during training. To demonstrate the approach in settings where no data from that data-generating process exists--as is often the case in applied prediction problems--we design a heterogeneous population of 883,320 novel games. AI agents are constructed using human data from a small set of conceptually related but structurally distinct "seed" games. In preregistered experiments, on average, agents predict initial human play in a random sample of 1,500 games from the population better than (i) a cognitive hierarchy model, (ii) game-theoretic equilibria, and (iii) out-of-the-box agents. For a small set of separate novel games, these simulations predict responses from a new sample of human subjects better even than the most plausibly relevant published human data.

Understanding how competitive pressure affects risk-taking is crucial in sequential decision-making under uncertainty. This study examines these effects using bench press competition data, where individuals make risk-based choices under pressure. We estimate the impact of pressure on weight selection and success probability. Pressure from rivals increases attempted weights on average, but responses vary by gender, experience, and rivalry history. Counterfactual simulations show that removing pressure leads many lifters to select lower weights and achieve lower success rates, though some benefit. The results reveal substantial heterogeneity in how competition shapes both risk-taking and performance.

Trading is a highly competitive task that requires a combination of strategy, knowledge, and psychological fortitude. With the recent success of large language models(LLMs), it is appealing to apply the emerging intelligence of LLM agents in this competitive arena and understanding if they can outperform professional traders. In this survey, we provide a comprehensive review of the current research on using LLMs as agents in financial trading. We summarize the common architecture used in the agent, the data inputs, and the performance of LLM trading agents in backtesting as well as the challenges presented in these research. This survey aims to provide insights into the current state of LLM-based financial trading agents and outline future research directions in this field.

Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understand the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis which solves significant memory and computing time limitations to extract crisis information from a correlation metric. Therefore, we focus on the recently investigated S&P500 mean market correlation in a period of roughly 20 years that includes the dot-com bubble, the global financial crisis and the Euro crisis. The analysis is performed two-fold: first, in retrospect on the whole dataset and second, in an on-line adaptive manner in pre-crisis segments. The on-line sensitivity horizon is roughly determined to be 80 up to 100 trading days after a crisis onset. A detailed comparison to global economic events supports the interpretation of the mean market correlation as an informative macroeconomic measure by a rather good agreement of change point distributions and major crisis events. Furthermore, the results hint to the importance of the U.S. housing bubble as trigger of the global financial crisis, provide new evidence for the general reasoning of locally (meta)stable economic states and could work as a comparative impact rating of specific economic events.

The analysis of market correlations is crucial for optimal portfolio selection of correlated assets, but their memory effects have often been neglected. In this work, we analyse the mean market correlation of the S&P500 which corresponds to the main market mode in principle component analysis. We fit a generalised Langevin equation (GLE) to the data whose memory kernel implies that there is a significant memory effect in the market correlation ranging back at least three trading weeks. The memory kernel improves the forecasting accuracy of the GLE compared to models without memory and hence, such a memory effect has to be taken into account for optimal portfolio selection to minimise risk or for predicting future correlations. Moreover, a Bayesian resilience estimation provides further evidence for non-Markovianity in the data and suggests the existence of a hidden slow time scale that operates on much slower times than the observed daily market data. Assuming that such a slow time scale exists, our work supports previous research on the existence of locally stable market states.

This paper examines how internal migration influences educational assortative mating patterns in India using Periodic Labour Force Survey data (2020-21). We analyze the association of migrant status and type of assortative mating, that is whether migrants are more likely to engage in homogamous (similar education) or heterogamous (different education) marriages compared to non-migrants. Results show migrants are significantly more likely to form educationally heterogamous marriages, with urban-to-urban migrants particularly prone to hypogamy (marrying higher-educated partners). These findings are validated using instrumental variables including crime rates, migrant networks, and unemployment rates. Family structure and marriage pool composition emerge as key mechanisms driving educational heterogamy among migrants, suggesting migration fundamentally alters marital formation preferences away from traditional educational homogamy patterns.

The standard framing treats structured human-data work as transitional, a bridge between today's imperfect models and a future state where automation is complete. We challenge this view by modeling structured human data as a persistent production input: evaluation, rubric-based judgment, auditing, exception handling, and continual updates that convert raw model capability into dependable, deployable performance. These activities accumulate into a reusable AI capability stock that raises productivity by improving reliability on existing tasks and by expanding the frontier of task families for which AI can be used at high confidence. Crucially, this capability stock depreciates as tasks and contexts drift, standards evolve, and new edge cases emerge. In a tractable baseline model, an interior steady state implies a closed-form, strictly positive long-run labor share devoted to structured human-data work whenever depreciation is positive, a "no last mile" result in which maintenance demand persists even as models improve. We then microfound aggregate capability with a portfolio of task families featuring diminishing returns, frontier entry, and complementarity, generating reallocation toward low-maturity and bottleneck families and a Roy-style mechanism for within-structured wage dispersion. Finally, we map model objects to observable proxies using standard data layers, and provide a conservative calibration suggesting a 5-7% steady-state structured labor share in the long run.

With the reversal of Roe v. Wade in 2022, many U.S. employers announced they would reimburse employees for abortion-related travel expenses. This action complements increasingly common employer policies subsidizing employee access to assisted reproductive technologies such as in-vitro fertilization and egg freezing. This article reflects on why employers offer these benefits and whether they enhance or undermine reproductive justice. From the employer's perspective, abortion and assisted reproductive technologies help women to plan childbearing around the demands of their jobs. Both are associated with delayed childbirth and reduced fertility, which lower the costs of motherhood to employers. However, firm subsidization of these services does not further reproductive justice because it reifies structures which incentivize women to delay childbirth and reduce fertility, and it reinforces economic and reproductive inequalities. We conclude by questioning whether reproductive justice is possible without transforming the economy so that it prioritizes care over profits.

This paper bridges reinforcement learning (RL) and risk-sensitive stochastic control by introducing a tractable exploration mechanism for policy search in risk-sensitive portfolio management, with known and unknown model parameters, that yields an endogenous relative-entropy regularization. We construct a discrete-time risk-sensitive benchmarked investment model. This model combines a factor-based asset universe with periodic portfolio rebalancing. Exploration is incorporated through user-specified Gaussian perturbations to baseline (exploitative) controls. The risk-sensitive stochastic control problem is solved analytically using the Free Energy-Entropy Duality. The Duality recasts the control problem as a linear-quadratic-Gaussian game and introduces a natural penalty for exploration. This approach yields simple sufficiency conditions for optimality. It also induces intuitive bounds on exploration based on risk sensitivity, asset covariance, and rebalancing frequency. Additionally, the optimal investment strategy can be interpreted through the lens of fractional Kelly strategies. By connecting risk-sensitive control theory and RL, this work provides a principled parametric family for policy-gradient implementations, guiding the design of RL methods.

Motivated by the widespread adoption of iterative project management techniques, we study the effects of workflow -- iterative or sequential -- on innovative behavior and performance. We conduct a series of laboratory experiments. Our first experiment shows that, in an open-ended creative challenge, iterative task completion leads to better outcomes than sequential task completion. In the second experiment we show that the advantage of iterative workflow further extends to innovation settings that do not involve idea generation. A key mechanism driving the advantage of iterative work is that it leads to frequent task switching, prompting workers to perform a broader search for the best available solution. In the third experiment we delve deeper into the search process and show that sequential work indeed leads to more myopic idea refinement behaviors, often ending in a (suboptimal) local maximum. Our results suggest that iterative workflow improves performance across multiple, structurally distinct innovation settings. We also identify three boundary conditions. First, iterative workflow helps achieve quick gains, but its performance advantage narrows over time. Therefore, workflow effects are stronger when balanced performance across project components is required, but weaker when excellence in one component can offset poor performance in others. Second, workflow has minimal effect on performance in tasks that do not require the worker to perform broad exploration. Third, workflow effects are minimal when workers complete the easier component first.

Surveys are critical inputs for research and policy, yet, enumerating a sampling frame is logistically infeasible or financially nonviable in many circumstances, such as during pandemics, natural disasters, or armed conflict. Respondent Driven Sampling (RDS) does not require a sampling frame, yet non-random peer recruitment often introduces substantial bias, particularly under high homophily. We introduce and evaluate Randomized Recruitment Driven Sampling (RRDS), a cellphone-based adaptation of RDS that incorporates researcher-controlled randomization into each recruitment wave. While standard RDS is necessary for stigmatized groups where network transparency is infeasible, RRDS is designed for low-stigma populations that become difficult to access due to logistical barriers. In these contexts, RRDS enforces the random recruitment assumption that traditional RDS relies upon but rarely achieves. Through simulation and an experiment surveying Bangladeshi garment workers during the COVID-19 pandemic, we demonstrate that RRDS produces less biased estimates and improved confidence interval coverage compared to traditional RDS. RRDS offers a scalable, remote-compatible alternative for studying low-stigma groups in challenging contexts where large-scale probability sampling is unsafe or infeasible.

Financial markets convert the incremental arrival of information into asset price changes. In a sandpile model grains of sand represent bits of data, and the size of an avalanche, governed by a scaling law, is linked to price volatility. While this model of self-organized criticality reproduces stylized facts, it also identifies a structural tension between the non-arbitrage condition and price adjustments consistent with a constant Sharpe ratio.

We analyze daily lead-time distributions for two Airbnb demand metrics, Nights Booked (volume) and Gross Booking Value (revenue), treating each day's allocation across 0-365 days as a compositional vector. The data span 2,557 days from January 2019 through December 2025 in a large North American region. Three findings emerge. First, GBV concentrates more heavily in mid-range horizons: beyond 90 days, GBV tail mass typically exceeds Nights by 20-50%, with ratios reaching 75% at the 180-day threshold during peak seasons. Second, Gamma and Weibull distributions fit comparably well under interval-censored cross-entropy. Gamma wins on 61% of days for Nights and 52% for GBV, with Weibull close behind at 38% and 45%. Lognormal rarely wins (<3%). Nonparametric GAMs achieve 18-80x lower CRPS but sacrifice interpretability. Third, generalized Pareto fits suggest bounded tails for both metrics at thresholds below 150 days, though this may partly reflect right-truncation at 365 days; above 150 days, estimates destabilize. Bai-Perron tests with HAC standard errors identify five structural breaks in the Wasserstein distance series, with early breaks coinciding with COVID-19 disruptions. The results show that volume and revenue lead-time shapes diverge systematically, that simple two-parameter distributions capture daily pmfs adequately, and that tail inference requires care near truncation boundaries.

Stablecoins promise par convertibility, yet issuers must balance immediate liquidity against yield on reserves to keep the peg credible. We study this treasury problem as a continuous-time control task with two instruments: reallocating reserves between cash and short-duration government bills, and setting a spread fee for either minting or burning the coin. Mint and redemption flows follow mutually exciting processes that reproduce clustered order flow. Peg deviations arise when immediate cash coverage is insufficient relative to outstanding supply, and the market price relaxes toward this liquidity-coverage fair value. We develop a stochastic model predictive control framework that incorporates moment closure for event intensities. Using Pontryagin's Maximum Principle, we show that the optimal reallocation control exhibits a soft-thresholding structure: no rebalancing occurs when the shadow-cost differential lies within a deadzone set by transaction costs, and reallocation scales linearly beyond that threshold up to a capacity-imposed saturation limit. Introducing settlement windows leads to a sampled-data implementation with a simple threshold (soft-thresholding) structure for rebalancing. We also establish a monotone stress-response property: as expected outflows intensify or windows lengthen, the optimal policy shifts predictably toward cash. In simulations covering various stress test scenarios, the controller preserves most bill carry in calm markets, builds cash quickly when stress emerges, and avoids unnecessary rotations under transitory signals. The proposed policy is implementation-ready and aligns naturally with operational cut-offs. Our results translate empirical flow risk into auditable treasury rules that improve peg quality without sacrificing avoidable carry.

The third National Charter School Study (NCSS III) aimed to test whether charter school were effective and to highlight outcomes on academic progress. The authors reported that typical charter school students outperformed similar students in non-charter public schools by 6 days in mathematics and 16 days in reading. This "days of learning" metric used to claim relatively higher performance in charter schools than in comparable public schools. This logic of this metric is critiqued in this paper, and an alternative method of reporting outcomes is proposed.

This paper is part of an ongoing investigation of "pragmatic information", defined in Weinberger (2002) as "the amount of information actually used in making a decision". Because a study of information rates led to the Noiseless and Noisy Coding Theorems, two of the most important results of Shannon's theory, we begin the paper by defining a pragmatic information rate, showing that all of the relevant limits make sense, and interpreting them as the improvement in compression obtained from using the correct distribution of transmitted symbols. The first of two applications of the theory extends the information theoretic analysis of the Kelly Criterion, and its generalization, the horse race, to a series of races where the stochastic process of winning horses, payoffs, and strategies depend on some stationary process, including, but not limited to the history of previous races. If the bettor is receiving messages (side information) about the probability distribution of winners, the doubling rate of the bettor's winnings is bounded by the pragmatic information of the messages. A second application is to the question of market efficiency. An efficient market is, by definition, a market in which the pragmatic information of the "tradable past" with respect to current prices is zero. Under this definition, markets whose returns are characterized by a GARCH(1,1) process cannot be efficient. Finally, a pragmatic informational analogue to Shannon's Noisy Coding Theorem suggests that a cause of market inefficiency is that the underlying fundamentals are changing so fast that the price discovery mechanism simply cannot keep up. This may happen most readily in the run-up to a financial bubble, where investors' willful ignorance degrade the information processing capabilities of the market.

The use of profiling evidence in criminal trials is a longstanding controversy in legal epistemology and evidence law theory. Many scholars, even when they oppose its use at trial, still assume that profiling evidence can be probative of guilt. We reject that assumption. Profiling evidence may support a generic hypothesis, but is not evidence that the defendant is guilty of the specific crime of which they are accused. We contrast profiling evidence with case-specific evidence, which speaks more directly to the facts of the case. Our critique departs from others by grounding the argument in a probabilistic analysis of evidentiary value. We also explore the implications of our account for debates about stereotyping.