arXiv subjects

Sandro Claudio Lera

Publications and source records attributed to Sandro Claudio Lera.

16 recordsLinked to original sources

arXiv2026

Limits of Predictability in Civil Litigation

Legal practice routinely relies on informal assessments of case strength, yet no large-scale empirical benchmark exists for how predictable civil-litigation outcomes actually are. Civil litigation unfolds through sequential filings, and parties may settle at any stage, yet most computational studies of legal prediction observe cases only after resolution, leaving open whether outcomes are predictable beforehand. Using 102{,}721 U.S.\ civil cases and 835{,}190 court filings from 1996 to 2022, we model each case as it evolves, predicting plaintiff win, plaintiff loss, or settlement at each stage from structured, textual, and institutional features available up to that point. The classifier achieves class-specific AUC values of 0.74--0.81 and up to 97\% accuracy for high-confidence predictions, providing a large-scale benchmark for litigation predictability before resolution. We characterize heterogeneity in predictability using case complexity, defined as the entropy of the predicted outcome distribution. Complexity is systematically higher in cases involving corporate parties and in cases only weakly anchored to precedent. Richer information improves prediction mainly in low-complexity cases, with diminishing returns as complexity rises: some disputes are hard to predict not for lack of information, but because their outcomes are genuinely less determinate. Complexity also rises as litigation progresses, indicating that additional filings can sustain or amplify uncertainty rather than resolve it. Settlement rates follow an inverted U-shape in complexity, peaking at intermediate uncertainty and declining at both extremes. These findings suggest that predictive uncertainty is not mere model error, but a structured signal of legal complexity, litigation dynamics, and how disputes are resolved.

cs.SI↗

arXiv2026

Strategic Network Abandonment

Socio-economic networks, from cities and firms to collaborative projects, often appear resilient for long periods before experiencing rapid, cascading decline as participation erodes. We explain such dynamics through a framework of strategic network abandonment, in which interconnected agents choose activity levels in a network game and remain active only if participation yields higher utility than an improving outside option. As outside opportunities rise, agents exit endogenously, triggering equilibrium readjustments that may either dissipate locally or propagate through the network. The resulting decay dynamics are governed by the strength of strategic complementarities, measuring how strongly an agent's incentives depend on the actions of others. When complementarities are weak, decay follows a heterogeneous threshold process analogous to bootstrap percolation: failures are driven by local neighborhoods, vulnerable clusters can be identified ex ante, and large cascades emerge only through bottom-up accumulation of fragility. When complementarities are strong, departures propagate globally, producing rupture-like dynamics characterized by metastable plateaus, abrupt system-wide collapse, and limited predictive power of standard spectral or structural indicators. The comparative effective of intervention depends on the strength of complementarity as well: Supporting central agents is most effective under strong complementarities, whereas targeting marginal agents is essential when complementarities are weak. Together, our results reveal how outside options, network structure, and strategic interdependence jointly determine both the fragility of socio-economic networks and the policies required to sustain them.

physics.soc-ph↗

arXiv2026

The Determinants of Judicial Promotion: Politics, Prestige, and Performance

Judicial promotions shape the composition of higher courts, yet their determinants remain poorly understood. This paper examines promotion from U.S. District Courts to Courts of Appeals using a discrete-time hazard framework that models annual promotion probability. Using a judge-year panel covering over 36,000 observations from 1930 to present, we incorporate career timing, political alignment, elite credentials, and judicial performance measures. Promotion probabilities follow a life-cycle pattern and are strongly influenced by political alignment between judges and presidents ($β$ = 2.12, p < 0.001). Elite credentials and productivity increase promotion likelihood, while higher reversal rates reduce it. Citation network centrality exhibits a meaningful association ($β$ = 0.230, p = 0.025) that operates independently of elite credentials. Promotion outcomes reflect a dynamic process shaped by timing, politics, elite networks, and performance signals, with political considerations dominating but not eclipsing judicial behavior.

cs.SI↗

arXiv2026

HawkesRank: Event-Driven Centrality for Real-Time Importance Ranking

Quantifying influence in networks is important across science, economics, and public health, yet widely used centrality measures remain limited: they rely on static representations, heuristic network constructions, and purely endogenous notions of importance, while offering little semantic connection to observable activity. We introduce HawkesRank, a dynamic framework grounded in multivariate Hawkes point processes that models exogenous drivers (intrinsic contributions) and endogenous amplification (self- and cross-excitation). This yields a principled, empirically calibrated, and adaptive importance measure. Classical indices such as Katz centrality and PageRank emerge as mean-field limits of the framework, clarifying both their validity and their limitations. Unlike static averages, HawkesRank measures importance through instantaneous event intensities, enabling prediction, transparent endo-exo decomposition, and adaptability to shocks. Using both simulations and empirical analysis of emotion dynamics in online communication platforms, we show that HawkesRank closely tracks system activity and consistently outperforms static centrality metrics.

cs.SI↗

arXiv2026

Why AI Alignment Failure Is Structural: Learned Human Interaction Structures and AGI as an Endogenous Evolutionary Shock

Recent reports of large language models (LLMs) exhibiting behaviors such as deception, threats, or blackmail are often interpreted as evidence of alignment failure or emergent malign agency. We argue that this interpretation rests on a conceptual error. LLMs do not reason morally; they statistically internalize the record of human social interaction, including laws, contracts, negotiations, conflicts, and coercive arrangements. Behaviors commonly labeled as unethical or anomalous are therefore better understood as structural generalizations of interaction regimes that arise under extreme asymmetries of power, information, or constraint. Drawing on relational models theory, we show that practices such as blackmail are not categorical deviations from normal social behavior, but limiting cases within the same continuum that includes market pricing, authority relations, and ultimatum bargaining. The surprise elicited by such outputs reflects an anthropomorphic expectation that intelligence should reproduce only socially sanctioned behavior, rather than the full statistical landscape of behaviors humans themselves enact. Because human morality is plural, context-dependent, and historically contingent, the notion of a universally moral artificial intelligence is ill-defined. We therefore reframe concerns about artificial general intelligence (AGI). The primary risk is not adversarial intent, but AGI's role as an endogenous amplifier of human intelligence, power, and contradiction. By eliminating longstanding cognitive and institutional frictions, AGI compresses timescales and removes the historical margin of error that has allowed inconsistent values and governance regimes to persist without collapse. Alignment failure is thus structural, not accidental, and requires governance approaches that address amplification, complexity, and regime stability rather than model-level intent alone.

cs.AI↗

arXiv2025

Social feedback amplifies emotional language in online video live chats

A growing share of human interactions now occurs online, where the expression and perception of emotions are often amplified and distorted. Yet, the interplay between different emotions and the extent to which they are driven by external stimuli or social feedback remains poorly understood. We calibrate a multivariate Hawkes self-exciting point process to model the temporal expression of six basic emotions in YouTube Live chats. This framework captures both temporal and cross-emotional dependencies while allowing us to disentangle the influence of video content (exogenous) from peer interactions (endogenous). We find that emotional expressions are up to four times more strongly driven by peer interaction than by video content. Positivity is more contagious, spreading three times more readily, whereas negativity is more memorable, lingering nearly twice as long. Moreover, we observe asymmetric cross-excitation, with negative emotions frequently triggering positive ones, a pattern consistent with trolling dynamics, but not the reverse. These findings highlight the central role of social interaction in shaping emotional dynamics online and the risks of emotional manipulation as human-chatbot interactions become increasingly realistic.

cs.SI↗

arXiv2025

Illusions of Criticality: Crises Without Tipping Points

Abrupt shifts in ecosystems, brains, markets, and climate are often diagnosed as signs of approaching a tipping point, i.e. a critical bifurcation where stability is lost. Here we reveal a broader and more deceptive mechanism: pseudo-bifurcations. In stochastic non-normal systems, asymmetric interactions produce transient episodes of apparent instability despite long-term stability. We show analytically, numerically, and with empirical evidence from brain dynamics during epileptic seizures that pseudo-bifurcations reproduce the full set of early-warning signals usually taken as proof of proximity to tipping points, including critical slowing down, increased variance, and dimensional collapse. Crucially, these false alarms can occur well before any true bifurcation, systematically biasing crisis diagnosis. This discovery reframes how abrupt transitions are interpreted across disciplines: what has long been attributed to ``criticality'' may instead reflect the hidden geometry of non-normal dynamics. By uncovering this illusion of criticality, we call for a fundamental reassessment of how crises are identified, predicted, and managed in natural, social, and technological systems.

nlin.CD↗

arXiv2025

Sensor optimization for urban wind estimation with cluster-based probabilistic framework

We propose a physics-informed machine-learned framework for sensor-based flow estimation for drone trajectories in complex urban terrain. The input is a rich set of flow simulations at many wind conditions. The outputs are velocity and uncertainty estimates for a target domain and subsequent sensor optimization for minimal uncertainty. The framework has three innovations compared to traditional flow estimators. First, the algorithm scales proportionally to the domain complexity, making it suitable for flows that are too complex for any monolithic reduced-order representation. Second, the framework extrapolates beyond the training data, e.g., smaller and larger wind velocities. Last, and perhaps most importantly, the sensor location is a free input, significantly extending the vast majority of the literature. The key enablers are (1) a Reynolds number-based scaling of the flow variables, (2) a physics-based domain decomposition, (3) a cluster-based flow representation for each subdomain, (4) an information entropy correlating the subdomains, and (5) a multi-variate probability function relating sensor input and targeted velocity estimates. This framework is demonstrated using drone flight paths through a three-building cluster as a simple example. We anticipate adaptations and applications for estimating complete cities and incorporating weather input.

cs.LG↗

arXiv2025

Data-Driven Law Firm Rankings to Reduce Information Asymmetry in Legal Disputes

Selecting capable counsel can shape the outcome of litigation, yet evaluating law firm performance remains challenging. Widely used rankings prioritize prestige, size, and revenue rather than empirical litigation outcomes, offering little practical guidance. To address this gap, we build on the Bradley-Terry model and introduce a new ranking framework that treats each lawsuit as a competitive game between plaintiff and defendant law firms. Leveraging a newly constructed dataset of 60,540 U.S. civil lawsuits involving 54,541 law firms, our findings show that existing reputation-based rankings correlate poorly with actual litigation success, whereas our outcome-based ranking substantially improves predictive accuracy. These findings establish a foundation for more transparent, data-driven assessments of legal performance.

cs.CY↗

arXiv2025

Community-centric modeling of citation dynamics explains collective citation patterns in science, law, and patents

Many human knowledge systems, such as science, law, and invention, are built on documents and the citations that link them. Citations, while serving multiple purposes, primarily function as a way to explicitly document the use of prior work and thus have become central to the study of knowledge systems. Analyzing citation dynamics has revealed statistical patterns that shed light on knowledge production, recognition, and formalization, and has helped identify key mechanisms driving these patterns. However, most quantitative findings are confined to scientific citations, raising the question of universality of these findings. Moreover, existing models of individual citation trajectories fail to explain phenomena such as delayed recognition, calling for a unifying framework. Here, we analyze a newly available corpus of U.S. case law, in addition to scientific and patent citation networks, to show that they share remarkably similar citation patterns, including a heavy-tailed distribution of sleeping beauties. We propose a holistic model that captures the three core mechanisms driving collective dynamics and replicates the elusive phenomenon of delayed recognition. We demonstrate that the model not only replicates observed citation patterns, but also better predicts future successes by considering the whole system. Our work offers insights into key mechanisms that govern large-scale patterns of collective human knowledge systems and may provide generalizable perspectives on discovery and innovation across domains.

physics.soc-ph↗

arXiv2024

Early Career Citations Capture Judicial Idiosyncrasies and Predict Judgments

Judicial impartiality is a cornerstone of well-functioning legal systems. We assemble a dataset of 112,312 civil lawsuits in U.S. District Courts to study the effect of extraneous factors on judicial decision making. We show that cases are randomly assigned to judges and that biographical judge features are predictive of judicial decisions. We use low-dimensional representations of judges' early-career citation records as generic representations of judicial idiosyncrasies. These predict future judgments with accuracies exceeding 65% for high-confidence predictions on balanced out-of-sample test cases. For 6-8% of judges, these representations are significant predictors across all judgments. These findings indicate that a small but significant group of judges routinely relies on extraneous factors and careful vetting of judges prior to appointment may partially address this issue. Our use of low-dimensional representations of citation records may also be generalized to other jurisdictions or to study other aspects of judicial decision making.

cs.SI↗

arXiv2021

Uncovering Feature Interdependencies in High-Noise Environments with Stepwise Lookahead Decision Forests

Conventionally, random forests are built from "greedy" decision trees which each consider only one split at a time during their construction. The sub-optimality of greedy implementation has been well-known, yet mainstream adoption of more sophisticated tree building algorithms has been lacking. We examine under what circumstances an implementation of less greedy decision trees actually yields outperformance. To this end, a "stepwise lookahead" variation of the random forest algorithm is presented for its ability to better uncover binary feature interdependencies. In contrast to the greedy approach, the decision trees included in this random forest algorithm, each simultaneously consider three split nodes in tiers of depth two. It is demonstrated on synthetic data and financial price time series that the lookahead version significantly outperforms the greedy one when (a) certain non-linear relationships between feature-pairs are present and (b) if the signal-to-noise ratio is particularly low. A long-short trading strategy for copper futures is then backtested by training both greedy and stepwise lookahead random forests to predict the signs of daily price returns. The resulting superior performance of the lookahead algorithm is at least partially explained by the presence of "XOR-like" relationships between long-term and short-term technical indicators. More generally, across all examined datasets, when no such relationships between features are present, performance across random forests is similar. Given its enhanced ability to understand the feature-interdependencies present in complex systems, this lookahead variation is a useful extension to the toolkit of data scientists, in particular for financial machine learning, where conditions (a) and (b) are typically met.

cs.LG↗

arXiv2019

Ordinal Tax To Sustain a Digital Economy

Recently, the French Senate approved a law that imposes a 3% tax on revenue generated from digital services by companies above a certain size. While there is a lot of political debate about economic consequences of this action, it is actually interesting to reverse the question: We consider the long-term implications of an economy with no such digital tax. More generally, we can think of digital services as a special case of products with low or zero cost of transportation. With basic economic models we show that a market with no transportation costs is prone to monopolization as minuscule, random differences in quality are rewarded disproportionally. We then propose a distance-based tax to counter-balance the tendencies of random centralisation. Unlike a tax that scales with physical (cardinal) distance, a ranked (ordinal) distance tax leverages the benefits of digitalization while maintaining a stable economy.

econ.GN↗

arXiv2019

A Theory of Discrete Hierarchies as Optimal Cost-Adjusted Productivity Organisations

Hierarchical structures are ubiquitous in human and animal societies, but a fundamental understanding of their raison d'être has been lacking. Here, we present a general theory in which hierarchies are obtained as the optimal design that strikes a balance between the benefits of group productivity and the costs of communication for coordination. By maximising a generic representation of the output of a hierarchical organization with respect to its design, the optimal configuration of group sizes at different levels can be determined. With very few ingredients, a wide variety of hierarchically ordered complex organisational structures can be derived. Furthermore, our results rationalise the ubiquitous occurrence of triadic hierarchies, i.e., of the universal preferred scaling ratio between $3$ and $4$ found in many human and animal hierarchies, which should occur according to our theory when production is rather evenly contributed by all levels. We also provide a systematic approach for optimising team organisation, helping to address the question of the optimal `span of control'. The significantly larger number $\sim 3-20$ of subordinates a supervisor typically manages is rationalised to occur in organisations where the production is essentially done at the bottom level and in which the higher levels are only present to optimise coordination and control.

physics.soc-ph↗

arXiv2017

GDP growth rates as confined Lévy flights

A new model that combines economic growth rate fluctuations at the microscopic and macroscopic level is presented. At the microscopic level, firms are growing at different rates while also being exposed to idiosyncratic shocks at the firm and sector level. We describe such fluctuations as independent Lévy-stable fluctuations, varying over multiple orders of magnitude. These fluctuations are aggregated and measured at the macroscopic level in averaged economic output quantities such as GDP. A fundamental question is thereby to what extend individual firm size fluctuations can have a noticeable impact on the overall economy. We argue that this question can be answered by considering the Lévy fluctuations as embedded in a steep confining potential well, ensuring nonlinear mean-reversal behavior, without having to rely on microscopic details of the system. The steepness of the potential well directly controls the extend towards which idiosyncratic shocks to firms and sectors are damped at the level of the economy. Additionally, the theory naturally accounts for business cycles, represented in terms of a bimodal economic output distribution, and thus connects two so far unrelated fields in economics. By analyzing 200 years of US GDP growth rates, we find that the model is in good agreement with the data.

q-fin.GN↗

arXiv2015

Currency target zone modeling: An interplay between physics and economics

We study the performance of the euro/Swiss franc exchange rate in the extraordinary period from September 6, 2011 and January 15, 2015 when the Swiss National Bank enforced a minimum exchange rate of 1.20 Swiss francs per euro. Based on the analogy between Brownian motion in finance and physics, the first-order effect of such a steric constraint would enter a priori in the form of a repulsive entropic force associated with the paths crossing the barrier that are forbidden. Non-parametric empirical estimates of drift and volatility show that the predicted first-order analogy between economics and physics are incorrect. The clue is to realise that the random walk nature of financial prices results from the continuous anticipations of traders about future opportunities, whose aggregate actions translate into an approximate efficient market with almost no arbitrage opportunities. With the Swiss National Bank stated commitment to enforce the barrier, traders's anticipation of this action leads to a vanishing drift together with a volatility of the exchange rate that depends on the distance to the barrier. We give direct quantitative empirical evidence that this effect is well described by Krugman's target zone model [P.R. Krugman. The Quarterly Journal of Economics, 106(3):669-682, 1991]. Motivated by the insights from this economical model, we revise the initial economics-physics analogy and show that, within the context of hindered diffusion, the two systems can be described with the same mathematics after all. Using a recently proposed extended analogy in terms of a colloidal Brownian particle embedded in a fluid of molecules associated with the underlying order book, we derive that, close to the restricting boundary, the dynamics of both systems is described by a stochastic differential equation with a very small constant drift and a linear diffusion coefficient.

q-fin.ST↗