We refute Taleb's claim that election forecasts are arbitrage-violating.
problem The validity of probabilistic election forecasts using no-arbitrage pricing techniques.
method We use mild assumptions to show all forecasts are arbitrage-free.
result Taleb's heuristic for evaluating forecasts is incorrect.
Data set tracks real-time election results for 4 hours post-October 2019 Portuguese elections.
problem Real-time tracking of election results for predictive modeling.
method Real-time data collection and interval-based analysis.
result Data set supports various predictive modeling tasks including numerical forecasting.
Improves stock market predictions on Election Day.
problem Predicting stock market volatility on Election Day.
method Combining large language models with specialized agents.
result EDSMF model improves S&P 500 prediction accuracy.
The prevalence of online media has attracted researchers from various domains to explore human behavior and make interesting predictions. In this research, we leverage heterogeneous social media data collected from various online platforms to predict Taiwan's 2016 presidential election. In contrast to most existing res…
Proposes a transfer learning framework to improve U.S. election prediction models.
problem Limited spatial data and spatial dependence challenges in presidential election prediction.
method Proposes a novel transfer learning framework within the SAR model, using a two-stage algorithm with transferring and debiasing stages.
result Substantially improves prediction accuracy and outperforms traditional methods in U.S. presidential swing states.
Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding …
Model election dynamics and disinformation impact using information flow.
problem Understanding the unpredictable evolution of election polls and disinformation effects.
method Model election dynamics by specifying the flow of relevant information, deriving poll statistics as outputs.
result Derived outputs (poll statistics) reveal insights into prediction, disinformation impact, and optimal information management strategies.
Remote sensing satellites capture the cyclic dynamics of our Planet in regular time intervals recorded in satellite time series data. End-to-end trained deep learning models use this time series data to make predictions at a large scale, for instance, to produce up-to-date crop cover maps. Most time series classificati…
Human stablecoin transactions predict political risk in cryptocurrency markets.
problem Predicting political risk in cryptocurrency markets.
method Structural break analysis and surrogate-based robustness tests.
result Human-driven stablecoin transactions shift significantly before major political events.
Fuzzy Forests reduces feature space in high-dimensional survey data.
problem High-dimensional and highly correlated datasets in social science.
method Fuzzy Forests algorithm, a variant of Random Forests.
result Partisan polarization was the strongest factor in the 2020 presidential election.
The goal of this research was to find a way to extend the capabilities of computers through the processing of language in a more human way, and present applications which demonstrate the power of this method. This research presents a novel approach, Rhetorical Analysis, to solving problems in Natural Language Processin…
Study uses Viber and street polls to estimate Belarus election ratings and turnout.
problem Obtaining accurate ratings of candidates in Belarus's banned polls.
method Bayesian multilevel regression with poststratification.
result Estimated ratings and turnout contradict official election results.
There is bountiful evidence that political uncertainty stemming from presidential elections or doubt about the direction of future policy make financial markets significantly volatile, especially in proximity to close elections or elections that may prompt radical policy changes. Although several studies have examined …
A framework traces ideology changes on social media during the 2016 U.S. election.
problem Understanding how ideology changed on social media during the 2016 U.S. election.
method Jointly estimating ideology of users and news sites, tracing changes over time.
result Both liberal and conservative users became more polarized over time.
We consider the estimation of binary election outcomes as martingales and propose an arbitrage pricing when one continuously updates estimates. We argue that the estimator needs to be priced as a binary option as the arbitrage valuation minimizes the conventionally used Brier score for tracking the accuracy of probabil…
This paper examines how voter concentration affects election outcomes in district-based systems.
problem How does the spatial concentration of electors impact election results?
method The authors frame the spatial distribution of electors in a probabilistic setting and explore models to capture intra-district polarization. They use Likelihood-free Inference under the Approximate Bayesian Computation framework and supervised regression methods to estimate parameters.
result The models can capture statistical properties of real elections and show how voter distributions can change election results.
A thermodynamic theory explains EU election vote distributions.
problem Describing EU election vote distributions.
method Tracing parallels between system energies of coupled nonlinear oscillators and party vote fractions.
result The Rayleigh-Jeans (RJ) theory well depicts EU vote results and candidate vote dispersion.
Calculates winning probability for three candidates based on support rates and information timing.
problem Determining optimal strategy for three candidates in an election.
method Closed-form solution using support rates, political spectrum positioning, time left, and information revelation rate.
result Optimal strategy can be complex, especially for candidates in the center of a polarized electorate.
The study uncovers stock synergy networks using mutual information in Indian stock market data.
problem Detecting clusters of stocks in synergy in the Indian Stock Market.
method Analysis of high frequency data, mutual information for non-linearity, comparison with correlation method.
result Mutual information method successfully identifies effective networks compared to correlation method.
We combine fine-grained spatially referenced census data with the vote outcomes from the 2016 US presidential election. Using this dataset, we perform ecological inference using distribution regression (Flaxman et al, KDD 2015) with a multinomial-logit regression so as to model the vote outcome Trump, Clinton, Other / …
In accordance with "Democracy's Effect on Development: More Questions than Answers", we seek to carry out a study in following the description in the 'Questions for Further Study.' To that end, we studied 33 countries in the Sub-Saharan Africa region, who all went through an election which should signal a "step-up" for…
Scores political leanings in Web3 betting markets.
problem Understanding political motivations in decentralized prediction markets.
method Constructing PBLS from Polymarket data, analyzing 15k addresses, 4k events, 8k markets.
result Validated PBLS through internal and external comparisons, revealing political and profit motives.
Prediction markets can shape political behavior through persistent signals, not just forecast accuracy.
problem The role of prediction markets beyond forecasting.
method Transaction-level evidence from the 2024 U.S. presidential election, Signal Credibility Index (SCI).
result Price signals in prediction markets are more influential due to persistence, breadth of trader types, and cross-platform consensus.
The paper uses Black-Scholes model to analyze political support and coalition agreements.
problem Determining the minimum support level for a minor party in a pre-electoral coalition.
method Modeling political support as a stochastic process with a deterministic growth rate and applying Black-Scholes option pricing theory.
result The minimum support level for a minor party to gain a representative in a pre-electoral coalition.
Paper proposes a faster neural machine translation model using election methods and Q-learning.
problem Slower inference time in complex attention models.
method Modelled attention network using election methods and Q-learning.
result Inference time is less than a standard Bahdanau translator, results comparable.
Study shows how high-budget agents can manipulate prediction markets.
problem Manipulation of prediction markets by high-budget agents.
method Agent-based simulations and analytic characterization of price dynamics.
result High-budget agents can temporarily shift prediction market prices.
Study of negative ads on social media during U.S. midterm elections.
problem Understanding the effectiveness and mechanisms of negative advertising on social media.
method Machine learning for sentiment analysis, AI image recognition, ordinal regressions.
result Negative ads are less effective than previously thought, anger is a key mechanism.
We propose using canonical correlation analysis (CCA) to generate features from sequences of medical billing codes. Applying this novel use of CCA to a database of medical billing codes for patients with diverticulitis, we first demonstrate that the CCA embeddings capture meaningful relationships among the codes. We th…
New results for modeling voter probabilities in elections.
problem Modeling voter preferences with aggregate data and individual covariates.
method Maximum likelihood estimation for Poisson binomial distribution, approximated with heteroscedastic Gaussian.
result Existence and curvature results for the MLE of the Poisson binomial likelihood.
PPI uses proxy data to improve inference from limited labels across related tasks.
problem Statistical inference with limited labels across multiple related tasks.
method Prediction-powered inference framework that uses cross-task recalibration to improve power and accuracy.
result Cross-task recalibration can substantially reduce confidence interval widths when labels are scarce.
New statistical models for predicting ranked preferences from partial orders.
problem Statistical models overlook information in list length.
method Composite and augmented ranking models for joint modeling of partial orders and list lengths.
result Augmented ranking models best predict both length and preferences.
Donald Trump was lagging behind in nearly all opinion polls leading up to the 2016 US presidential election, but he surprisingly won the election. This raises the following important questions: 1) why most opinion polls were not accurate in 2016? and 2) how to improve the accuracies of opinion polls? In this paper, we …
Geometric Graph Alignment enhances IoT intrusion detection using NID data.
problem Data scarcity hinders IoT intrusion detection accuracy.
method Geometric Graph Alignment (GGA) approach to transfer knowledge between network intrusion detection and IoT intrusion detection domains.
result GGA approach boosts IoT intrusion detection performance on multiple datasets.
Detects anomalous behavior in social media users by analyzing content and connections.
problem Identifying disruptive patterns in user behavior on social media platforms.
method Joint representation learning of content and connection to detect anomalous behavior.
result Observed densely connected users engaging in local politics and exhibiting troll-like behavior.
Prediction markets are used in real life to predict outcomes of interest such as presidential elections. This paper presents a mathematical theory of artificial prediction markets for supervised learning of conditional probability estimators. The artificial prediction market is a novel method for fusing the prediction …
Stratifying patients at risk for postoperative complications may facilitate timely and accurate workups and reduce the burden of adverse events on patients and the health system. Currently, a widely-used surgical risk calculator created by the American College of Surgeons, NSQIP, uses 21 preoperative covariates to asse…
Crowd opinions in microblogs can predict event outcomes, matching with expert opinions.
problem Utilizing crowd wisdom for event outcome prediction in microblogs.
method Multi-label sentiment classification of tweets to gauge crowd opinion and compare with expert predictions.
result Crowd opinions in microblogs often match with expert opinions, especially in non-debate events.
New method evaluates language model forecasters by checking consistency of predictions.
problem Evaluating the performance of language model forecasters is difficult due to lack of ground truth.
method Developed a consistency check framework based on arbitrage to evaluate forecasters.
result Consistency metrics correlate with ground truth performance of LLM forecasters.
Bayesian estimators for causal inference using hierarchical Gaussian Processes.
problem Estimating causal effects in sharp and fuzzy RD/RK designs.
method Hierarchical Gaussian Process models for regression and classification.
result Hierarchical GP models improve precision and coverage of RD/RK estimations.
New voting strategies show committee-based consensus can scale efficiently.
problem Ensuring honest committees in committee-based consensus protocols.
method Empirical analysis of simpler voting strategies and their convergence to optimality.
result Simpler voting strategies converge to optimality exponentially quickly, ensuring robustness and efficiency.
Social dynamics is concerned primarily with interactions among individuals and the resulting group behaviors, modeling the temporal evolution of social systems via the interactions of individuals within these systems. In particular, the availability of large-scale data from social networks and sensor networks offers an…
Estimates mode of discrete distributions with fewer samples.
problem Identifying the mode of a discrete distribution with high probability.
method Generalizes PPR martingale confidence sequences to handle multiple modes.
result PPR-1v1 stopping rule is asymptotically optimal and significantly more efficient.
We present a new modeling technique for solving the problem of ecological inference, in which individual-level associations are inferred from labeled data available only at the aggregate level. We model aggregate count data as arising from the Poisson binomial, the distribution of the sum of independent but not identic…
Bayesian nonparametric models for data with heterogeneous particles.
problem Deconvolving data with heterogeneous particles, like voter tallies in elections.
method Nonparametric deconvolution models (NDMs) using two tiers of Dirichlet processes.
result NDMs can recover how factor distributions vary locally for each observation.
Unified kernel for prediction markets reduces belief variance forecast error.
problem Lack of standardized tools for quoting and hedging belief risk in prediction markets.
method Logit jump-diffusion model with risk-neutral drift, calibration pipeline, and coherent derivative layer.
result Model reduces forecast error compared to diffusion-only and probability-space baselines.
The paper explores learning from label proportions, showing differences in efficiency between LLP and PAC learning.
problem Learning from label proportions (LLP) in unlabeled data with given label proportions.
method Formal definition and computational complexity analysis of LLP learning.
result LLP learning is more restrictive than PAC learning for finite VC classes, and some classes are uncharacterizable.
There certainly is little or no doubt that politicians, sometimes consciously and sometimes not, exert a significant impact on stock markets. The evolving volatility over the Republican Donald Trump's surprise victory in the US presidential election is a perfect example when politicians, through announced policies, sen…
Bayesian DL model improves DCMs for better predictive and inferential performance.
problem Limited interpretability and predictive underperformance of traditional DCMs.
method Integrates deep learning with approximate Bayesian inference (SGLD).
result Improves predictive and inferential metrics in discrete choice models.