Crowd wisdom improves decision-making by integrating multiple responses.
problem Improving collective decision-making accuracy from multiple responses.
method Analogous to unsupervised dimension reduction, using techniques like PCA and Isomap.
result Crowd wisdom methods outperform existing solutions, including supervised learning.
Institutional investors' average portfolio structure shows collective rationality, or Wisdom of the Crowd.
problem Transaction costs in financial markets.
method Analysis of institutional investors' portfolio structures and deviations from rational benchmarks.
result Institutional investors display collective rationality, or Wisdom of the Crowd, without needing nearly rational individuals.
Contributes to economics theory through learning and crowd wisdom.
problem Rebuilding economics theory from econophysics insights.
method Focuses on aggregation, individual and collective learning, and wisdom of crowds.
result Econophysics can contribute to economics theory through learning and crowd wisdom.
WOCCE uses crowd wisdom to improve clustering decisions.
problem Improving clustering decisions through group wisdom.
method A feedback framework for cluster ensemble that evaluates decentralization, independence, and diversity criteria.
result WOCCE outperforms other algorithms in aggregate decision-making.
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.
Crowdsourced wisdom improves causal learning.
problem Improving causal learning through collective intelligence.
method Crowdsourcing, expert knowledge elicitation, aggregation techniques, and LLMs.
result Collective contributions enhance global causal structure.
Proposes methods to improve wisdom of crowds by considering worker diversity and correlations.
problem Improving wisdom of crowds by considering worker diversity and correlations.
method Proposes inference, learning, and teaching methods considering worker diversity and correlations.
result Proposes methods to improve wisdom of crowds by considering worker diversity and correlations.
Study predicts Euroleague basketball games with simple models, less accurate than crowds.
problem Predicting Euroleague basketball games with machine learning.
method Extracted features from Euroleague data, applied supervised machine learning techniques.
result Simple machine learning models achieve accuracy of less than 67% on test sets, outperformed by crowds.
Framework uses WOC theory to improve clustering ensemble performance.
problem Improving clustering ensemble performance in data analysis.
method Exploits WOC theory's four conditions (diversity, independency, decentralization, aggregation) to guide clustering and combination.
result Superior performance compared to state-of-the-art methods on varied data sets.
Crowdsourcing improves mobile sensing through social network trust.
problem Improving mobile sensing performance using agent-participatory data.
method Introducing crowdsourcing methods inspired by social networks.
result Improved mobile sensing performance via crowdsourcing.
Lower bounds show OLS outperforms basis pursuit in overparameterized linear regression.
problem Excess risk of sparse interpolating procedures in overparameterized linear regression.
method Proved lower bounds on excess risk for OLS and basis pursuit.
result Excess risk of basis pursuit can converge at an exponentially slower rate than OLS.
The paper proposes a new method for learning reward models from ordinal feedback, improving upon binary feedback.
problem Learning reward models from human preferences using binary feedback discards useful samples and loses fine-grained information.
method The paper introduces a framework for learning reward models under ordinal feedback, generalizing the Bradley-Terry model.
result Ordinal feedback reduces the Rademacher complexity compared to binary feedback, leading to better reward learning.
New method identifies informed traders in prediction markets.
problem How information is incorporated into market prices is unknown.
method Kyle model applied to field experiment prediction market data.
result Traders with significant price impact are identified as informed.
Cost forecasting helps crowdsourcers manage growing task sets efficiently.
problem Crowdsourcers face resource overwhelm with growing task sets.
method Cost forecasting to decide between new tasks or existing ones based on cost efficiency.
result Cost forecasting improves accuracy and efficiency in crowdsourcing.
Ensemble methods get better with more models if loss function is convex.
problem Whether ensemble methods improve with more models.
method Analyzing different ensemble methods and loss functions.
result Ensembles get better with more models only if the loss function is convex.
Ensembles of neural networks learn better by sharing information.
problem Improving performance of neural networks through collective learning.
method Modeling neural networks as socially interacting agents aiming to maximize their own performance and functional relations to others.
result Optimal collective performance emerges from local interactions between networks, leading to specialization and higher confidence.
When dealing with subjective, noisy, or otherwise nebulous features, the "wisdom of crowds" suggests that one may benefit from multiple judgments of the same feature on the same object. We give theoretically-motivated `feature multi-selection' algorithms that choose, among a large set of candidate features, not only wh…
Supervised method improves crowdsourcing accuracy.
problem Unsupervised crowdsourcing ignores labeler reliability.
method Saddle point algorithm to identify reliable labelers.
result Supervised method outperforms unsupervised methods.
Machine learning ensemble improves accuracy by considering minority answers as more likely true.
problem Ensemble methods often rely on majority voting, which can fail when the majority is wrong.
method Proposes Bayesian Truth Serum for classification problems, detecting surprising majority answers.
result Better classification performance achieved by considering minority answers as more likely true.
Crowdsourcing estimates labels from group intuitions.
problem Difficulty in traditional labeling for complex data.
method Ballpark Learning for group-based inference.
result Crowd estimates rival supervised models.
BaMANI uses ensemble learning to improve Bayesian network inference.
problem Bayesian network inference's reliance on specific algorithms can obscure causal relationships.
method Developed an ensemble learning approach to marginalize algorithm impact.
result Improved accuracy and reliability of causal network predictions.
Bayesian scheme optimally learns worker quality in crowdsourced regression.
problem Learning the quality of workers in crowdsourced regression tasks.
method Iterative Bayesian learning approach.
result Proves optimal mean squared error performance.
MRCNet tackles crowd counting and density mapping in aerial imagery.
problem Accurate crowd counting and density estimation in aerial imagery.
method MRCNet is a novel encoder-decoder CNN that combines VGG-16 with FPN-inspired lateral connections.
result MRCNet outperforms state-of-the-art methods in aerial and CCTV-based crowd counting.
It is common for CCTV operators to overlook inter- esting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveil…
Max-MIG tackles crowdsourced label learning without knowing crowd information structure.
problem Learning from crowds without knowing the information structure among crowds.
method Max-MIG is an information theoretic approach that simultaneously aggregates crowdsourced labels and learns a data classifier.
result Max-MIG achieves state-of-the-art results in most settings, including real-world data.
In evaluating prediction markets (and other crowd-prediction mechanisms), investigators have repeatedly observed a so-called "wisdom of crowds" effect, which roughly says that the average of participants performs much better than the average participant. The market price---an average or at least aggregate of traders' b…
We found that factors decay over time, with momentum fitting best.
problem Understanding how factors decay over time and their impact on performance.
method Derived a hyperbolic decay model for factors, tested against linear and exponential alternatives.
result Momentum exhibits hyperbolic decay, outperforming linear and exponential models.
Study shows how 'crowding' in equity trading affects performance and costs.
problem Deterioration of strategy performance, increased trading costs, and systemic risk due to equity factor crowding.
method Direct metrics of crowding based on imbalances of trades executed on the market, analyzing U.S. equity market data.
result Significant signs of crowding in well-known equity signals, especially Momentum, affecting order flow and portfolio rebalancing.
Model predicts synchronized crowd behavior with tipping points.
problem Understanding and predicting synchronized crowd behavior.
method Introduces order parameter and identifies tipping point.
result Crowd behavior is driven by active, volatile agents.
MTCNet uses MTL to estimate crowd density and count.
problem Crowd count estimation challenges due to scale variations and perspective.
method MTL deep neural network architecture with two tasks: density estimation and count classification.
result Achieves lower MAE than state-of-the-art methods on multiple datasets.
Bayesian approach improves crowdsourcing predictions.
problem Estimating continuous labels from crowdsource workers.
method Variational Bayesian technique for worker noise models.
result Bayesian approaches significantly outperform non-Bayesian methods.
ACFM predicts crowd flow adaptively integrating various factors.
problem Adaptive integration of factors affecting crowd flow changes.
method Unified neural network module with attention mechanism.
result Significant improvements over state-of-the-art methods.
New method improves crowd counting accuracy using inverse k-NN maps and multiscale upsampling.
problem Improving accuracy of crowd density maps for high-density gatherings.
method Developed MUD-ikNN architecture using inverse k-NN maps and multiscale upsampling. result New network architecture outperforms state-of-the-art crowd counting.
Crowded trades cluster investors, affecting stock price stability.
problem Crowded trades lead to price instability and systemic risk.
method Market clustering measure using granular trading data.
result Market clustering has a causal effect on stock return distribution tails, especially positive tail.
System detects social interactions in crowds using mobile phone sensors.
problem Detecting social interactions in crowded settings.
method Multi-modal mobile sensing (BLE, accelerometer, gyroscope) and machine learning.
result 77.8% precision and 86.5% recall for predicting social interactions.
WISDoM uses the Wishart distribution to analyze neurological data like EEG and brain connectivity.
problem Characterizing deviations of covariance or correlation matrices from expected values.
method WISDoM framework for quantifying deviations from the Wishart distribution.
result Validated on EEG feature ranking and classification of autism subjects.
Paper proposes semi-supervised methods for faster, more scalable message stance classification.
problem Determining the veracity of messages in social networks.
method Uses Label Propagation and Label Spreading algorithms for semi-supervised learning.
result Semi-supervised learning outperforms supervised models in terms of accuracy, speed, and scalability.
Machine learning outperforms crowd investors in predicting loan defaults and investment returns.
problem Determining if machine learning can outperform human decision-making in crowd lending.
method Using data from Prosper.com, a sophisticated ML algorithm was trained to predict loan defaults and investment returns.
result The ML algorithm outperforms crowd investors in predicting loan defaults and investment returns, especially for risky loans.
The paper interprets financial markets as crowds during booms and busts.
problem Understanding market irrationality during booms and busts.
method Integrates crowd psychology into behavioural finance.
result Markets behave like psychological crowds during booms and busts.
A model-free hedging method using stock crowding scores.
problem Designing costless portfolio strategies to hedge market risk.
method Network analysis of fund holdings to compute crowding scores, constructing long-short portfolios without numerical optimization.
result Long-short portfolios provide protection against both small and large market price fluctuations.
The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semant…
Investment herding can reduce household consumption, a phenomenon called crowding-out effect.
problem Investment herding's impact on household consumption.
method Optimal control theory to model and solve for household investment and consumption decisions.
result Existence of crowding-out effect due to investment herding.
CENs learn interpretable embeddings from images considering worker biases and visual context.
problem Learning interpretable embeddings from noisy crowd annotations.
method Context Embedding Networks (CENs) model worker biases and visual context.
result CENs produce more interpretable embeddings than existing approaches.
Proposes a new model for aggregating crowd-labeled data.
problem Aggregating and denoising crowd-labeled data.
method Permutation-based model with error metric and minimax rates.
result Global minimax rates match lower bounds and efficient estimators designed.
New method addresses crowding in high-dimensional data visualization.
problem Crowding issue in visualizing high-dimensional data.
method Adjusting capacity of high-dimensional balls and estimating correlation dimension.
result Mitigates crowding in various distance metrics.
Crowdsourcing can improve scientific investigation by enabling reproducibility and transparency.
problem Current research methods lack reproducibility and transparency, leading to unreliable decisions.
method Next-generation investigative approach leveraging human diversity, micro-specialized crowds, and computer-assisted control methods.
result The Theory of Enablers provides specific cognitive and non-cognitive enablers for crowd-based scientific investigation.
Enhances crowd safety through AI and data-driven models.
problem Improving crowd safety during events.
method Innovative data collection, AI, and machine learning.
result Accurate multi-day forecasts for event planning.
Bayesian model improves truth inference from highly redundant crowd annotations.
problem Inferring true annotations from highly redundant crowd annotations.
method Bayesian graphical model with conjugate priors and iterative expectation-maximisation inference.
result Our technique significantly outperforms majority vote heuristic at one-sided level 0.025.