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We consider the problem of online preemptive scheduling on a single machine to minimize the total flow time. In clairvoyant scheduling, where job processing times are revealed upon arrival, the Shortest Remaining Processing Time (SRPT) algorithm is optimal. In practice, however, exact processing times are often unknown. At the opposite extreme, non-clairvoyant scheduling, in which processing times are revealed only upon completion, suffers from strong lower bounds on the competitive ratio. This motivates the study of intermediate information models. We introduce a new model in which processing times are revealed gradually during execution. Each job consists of a sequence of operations, and the processing time of an operation becomes known only after the preceding one completes. This models many scheduling scenarios that arise in computing systems. Our main result is a deterministic $O(m^2)$-competitive algorithm, where $m$ is the maximum number of operations per job. More specifically, we prove a refined competitive ratio in $O(m_1 \cdot m_2)$, where $m_1$ and $m_2$ are instance-dependent parameters describing the operation size structure. Our algorithm and analysis build on recent

Human decision makers increasingly delegate choices to AI agents, raising a natural question: does the AI implement the human principal's preferences or pursue its own? To study this question using revealed preference techniques, I introduce the Luce Alignment Model, where the AI's choices are a mixture of two Luce rules, one reflecting the human's preferences and the other the AI's. I show that the AI's alignment (similarity of human and AI preferences) can be generically identified in two settings: the laboratory setting, where both human and AI choices are observed, and the field setting, where only AI choices are observed.

I develop a revealed preference framework to test whether an aggregate allocation of indivisible objects satisfies Pareto efficiency and individual rationality (PI) without observing individual preferences. Exploiting the type-based preferences of Echenique et. al. (2013), I derive necessary and sufficient conditions for PI-rationalizability. I show that an allocation is PI-rationalizable if and only if its allocation graph is acyclic. Next, I analyse non-PI-rationalizable allocations. First, I study the three respective problems: removal of a minimum size of subset of individuals/types/objects to restore PI-rationalizability. I prove that these three problems are NP-complete. Then, I provide an alternative goodness-of-fit measure, namely Critical Exchange Index (CEI). The CEI assess the highest portion of individuals who can involve exchanging their final objects to reach PI. This measure shows the extent of inefficiencies. The results yield the first complete revealed preference analysis for Pareto efficiency and individual rationality in matching markets and provide an implementable tool for empirical applications.

Economic complexity measures aim to quantify the capability content or endowment of industries and territories; however, capabilities are not observable, and therefore cannot be directly used in the computations. We estimate such endowments by quantifying the quality and diversity of the skills in the occupations required in specific industries. We refer to this job-based assessment as the hidden complexity, in contrast with the usual revealed complexity, which is computed from economic outputs such as exports or production. We show that our job-based measure of complexity is positively associated to wage levels and labor productivity growth, whereas the classic revealed measure is not. Finally, we discuss the application of these methods at the territorial level, showing their connection with economic growth.

Dense cores in massive, parsec-scale molecular clumps are sites that harbor protocluster formation. We present results from observations towards a hub-filament structure of a massive Infrared Dark Cloud (IRDC) G14.225-0.506 using the Atacama Large Millimeter/submillimeter Array (ALMA). The dense cores are revealed by the 1.3 mm dust continuum emission at an angular resolution of $\sim$ 1.5'' and are identified through the hierarchical Dendrogram technique. Combining with the N$_2$D$^+$ 3-2 spectral line emission and gas temperatures derived from a previous NH$_3$ study, we analyze the thermodynamic properties of the dense cores. The results show transonic and supersonic-dominated turbulent motions. There is an inverse correlation between the virial parameter and the column density, which implies that denser regions may undergo stronger gravitational collapse. Molecular outflows are identified in the CO 2-1 and SiO 5-4 emission, indicating active protostellar activities in some cores. Besides these star formation signatures revealed by molecular outflows in the dense cores, previous studies in the infrared, X-ray, and radio wavelengths also found a rich and wide-spread population of

Regimes routinely conceal acts of repression. We show that observed repression may be negatively correlated with total repression, consisting of both revealed and concealed acts. This distortion can generate perverse effects for policy interventions designed to reduce repression and complicates inference about the causes and consequences of repression. We develop a model in which regimes choose whether to conceal repression and activists decide whether to challenge the regime. We identify two measurement problems - one due to concealment and one to deterrence. We construct indices of repression that account for these problems and show how these indices can be expressed in terms of observable variables by leveraging equilibrium relationships. We then propose an empirical strategy to estimate these indices. As a proof of concept, we apply this approach to Russia, estimating repression indices at a monthly frequency for 2020-2025.

This paper unifies two key results from economic theory, namely, revealed rational inattention and classical revealed preference. Revealed rational inattention tests for rationality of information acquisition for Bayesian decision makers. On the other hand, classical revealed preference tests for utility maximization under known budget constraints. Our first result is an equivalence result - we unify revealed rational inattention and revealed preference through an equivalence map over decision parameters and partial order for payoff monotonicity over the decision space in both setups. Second, we exploit the unification result computationally to extend robustness measures for goodness-of-fit of revealed preference tests in the literature to revealed rational inattention. This extension facilitates quantifying how well a Bayesian decision maker's actions satisfy rational inattention. Finally, we illustrate the significance of the unification result on a real-world YouTube dataset comprising thumbnail, title and user engagement metadata from approximately 140,000 videos. We compute the Bayesian analog of robustness measures from revealed preference literature on YouTube metadata featu

Construction grammar posits that constructions, or form-meaning pairings, are acquired through experience with language (the distributional learning hypothesis). But how much information about constructions does this distribution actually contain? Corpus-based analyses provide some answers, but text alone cannot answer counterfactual questions about what \emph{caused} a particular word to occur. This requires computable models of the distribution over strings -- namely, pretrained language models (PLMs). Here, we treat a RoBERTa model as a proxy for this distribution and hypothesize that constructions will be revealed within it as patterns of statistical affinity. We support this hypothesis experimentally: many constructions are robustly distinguished, including (i) hard cases where semantically distinct constructions are superficially similar, as well as (ii) \emph{schematic} constructions, whose ``slots'' can be filled by abstract word classes. Despite this success, we also provide qualitative evidence that statistical affinity alone may be insufficient to identify all constructions from text. Thus, statistical affinity is likely an important, but partial, signal available to lea

Afriat's Theorem (1967) states that a dataset can be thought of as being generated by a consumer maximizing a continuous and increasing utility function if and only if it is free of revealed preference cycles containing a strict relation. The latter property is often known by its acronym, GARP (for generalized axiom of revealed preference). This paper surveys extensions and applications of Afriat's seminal result. We focus on those results where the consistency of a dataset with the maximization of a utility function satisfying some property can be characterized by a suitably modified version of GARP.

The linear-in-means model is the standard empirical model of peer effects. Using choice data and exogenous group variation, we first develop a revealed preference style test for the linear-in-means model. This test is formulated as a linear program and can be interpreted as a no money pump condition with an additional incentive compatibility constraint. We then study the identification properties of the linear-in-means model. A key takeaway from our analysis is that there is a close relationship between the dimension of the outcome variable and the identifiability of the model. Importantly, when the outcome variable is one-dimensional, failures of identification are generic. On the other hand, when the outcome variable is multi-dimensional, we provide natural conditions under which identification is generic.

Elucidating long-range interaction guided organization of matter is a fundamental question in physical systems covering multiple length scales. Here, based on the hexagonal disk model, we analyze the characteristic inhomogeneity created by long-range repulsions, and reveal the intrinsic conformal order in particle packings in mechanical equilibrium. Specifically, we highlight the delicate angle-preserved bending of the lattice to match the inhomogeneity condition. The revealed conformal order is found to be protected by the surrounding topological defects. These results advance our understanding on long-range interacting systems, and open the promising possibilities of using long-range forces to create particle packings not accessible by short-range forces, which may have practical consequences.

The weak axiom of revealed preference (WARP) ensures that the revealed preference (i) is a preference relation (i.e., it is complete and transitive) and (ii) rationalizes the choices. However, when WARP fails, either one of these two properties is violated, but it is unclear which one it is. We provide an alternative characterization of WARP by showing that WARP is equivalent to the conjunction of two axioms each of which separately guarantees (i) and (ii).

This paper develops a framework to study the statistical power of revealed-preference tests. With randomly sampled budgets and mild smoothness of demand, statistical learning implies that any model consistent with the data must approximate true choice behaviour. We interpret this result as follows: passing a revealed-preference test is informative only to the extent that the data are sufficiently rich to rule out economically meaningful departures from the maintained model. We make this precise by linking sample size and confidence to the magnitude of detectable departures, and by characterising how power rises with additional observations. Extending our approach beyond revealed-preference inequalities to smooth functional restrictions yields practical tests, even when exact revealed-preference tests are computationally infeasible. We also provide confidence intervals for smooth functionals of demand, including welfare effects. Simulations show that standard sample sizes can generate widely different power across models, contextualizing why some conditions ``rarely reject'' in practice.

We demonstrate that a quiet state and large-amplitude self-sustained oscillations can co-exist in a carbon nanotube subject to time-independent drive. A feature of the bistability is that it would be hysteresis-free in the absence of noise and the oscillatory state would not be seen. It is revealed by random switching between the stable states, which we observe in the time domain. We attribute the switching to fluctuations in the system and show that it displays Poisson statistics. We propose a minimalistic model that relates the emergence of the bistability to a non-monotonic variation of nonlinear friction with the vibration amplitude. This new type of dynamical regime and the means to reveal it are generic and are of interest for various mesoscopic vibrational systems.

We investigated femtosecond laser ablation dynamics using THz time-domain spectroscopy. To clarify the breakdown dynamics of materials, we focused on the motion of charged particles and measured the terahertz waves emitted during laser ablation. We revealed that the Coulomb force dominated the ablation process. Furthermore, comparisons of the experimental results with theoretical models showed that material breakdown occurs within a few hundred femtoseconds. Our experimental results indicate that electrostatic ablation is the most likely ablation mechanism for metals.

Given a data-set of consumer behaviour, the Revealed Preference Graph succinctly encodes inferred relative preferences between observed outcomes as a directed graph. Not all graphs can be constructed as revealed preference graphs when the market dimension is fixed. This paper solves the open problem of determining exactly which graphs are attainable as revealed preference graphs in $d$-dimensional markets. This is achieved via an exact characterization which closely ties the feasibility of the graph to the Matrix Sign Rank of its signed adjacency matrix. The paper also shows that when the preference relations form a partially ordered set with order-dimension $k$, the graph is attainable as a revealed preference graph in a $k$-dimensional market.

Quantum teleportation is the name of a problem: how can the real-valued parameters encoding the state at Alice's location make their way to Bob's location via shared entanglement and only two bits of classical communication? Without an explanation, teleportation appears to be a conjuring trick. Investigating the phenomenon with Schrödinger states and reduced density matrices shall always leave loose ends because they are not local and complete descriptions of quantum systems. Upon demonstrating that the Heisenberg picture admits a local and complete description, Deutsch and Hayden rendered its explanatory power manifest by revealing the trick behind teleportation, namely, by providing an entirely local account. Their analysis is re-exposed and further developed.

Consider the object allocation (one-sided matching) model of Shapley and Scarf (1974). When final allocations are observed but agents' preferences are unknown, when might the allocation be in the core? This is a one-sided analogue of the model in Echenique, Lee, Shum, and Yenmez (2013). I build a model in which the strict core is testable -- an allocation is "rationalizable" if there is a preference profile putting it in the core. In this manner, I develop a theory of the revealed preferences of one-sided matching. I study rationalizability in both non-transferrable and transferrable utility settings. In the non-transferrable utility setting, an allocation is rationalizable if and only if: whenever agents with the same preferences are in the same potential trading cycle, they receive the same allocation. In the transferrable utility setting, an allocation is rationalizable if and only if: there exists a price vector supporting the allocation as a competitive equilibrium; or equivalently, it satisfies a cyclic monotonicity condition. The proofs leverage simple graph theory and combinatorial optimization and tie together classic theories of consumer demand revealed preferences and co

An observer wants to understand a decision-maker's welfare from her choice. She believes that decisions are made under limited attention. We argue that the standard model of limited attention cannot help the observer greatly. To address this issue, we study a family of models of choice under limited attention by imposing an attention floor in the decision process. We construct an algorithm that recovers the revealed preference relation given an incomplete data set in these models. Next, we take these models to the experimental data. We first show that assuming that subjects make at least one comparison before finalizing decisions (that is, an attention floor of 2) is almost costless in terms of describing the behavior when compared to the standard model of limited attention. In terms of revealed preferences, on the other hand, the amended model does significantly better. We can not recover any preferences for 63% of the subjects in the standard model, while the amended model reveals some preferences for all subjects. In total, the amended model allows us to recover one-third of the preferences that would be recovered under full attention.