Income redistribution boosts cooperation in spatial public goods games, but the effect depends on the model of redistribution.
problem The impact of income redistribution on cooperation in spatial public goods games.
method Two models of income redistribution were used: local and global. Spatial public goods games were employed to study the evolution of cooperation.
result The cooperation level is significantly higher with local redistribution of income, but lower with global redistribution.
The study examines how individual ability and project benefit influence cooperation in public goods games.
problem Maximizing cooperation in public goods games with varying individual contributions and benefits.
method A generalized public goods game model incorporating individual ability and project benefit.
result The upper limit of individual benefit promotes cooperation, while the upper limit of individual contribution inhibits it.
In this paper we study continuous-time stochastic control problems with both monotone and classical controls motivated by the so-called public good contribution problem. That is the problem of n economic agents aiming to maximize their expected utility allocating initial wealth over a given time period between private …
We propose an extended public goods interaction model to study the evolution of cooperation in heterogeneous population. The investors are arranged on the well known scale-free type network, the Barabási-Albert model. Each investor is supposed to preferentially distribute capital to pools in its portfolio based on the …
Potential games, originally introduced in the early 1990's by Lloyd Shapley, the 2012 Nobel Laureate in Economics, and his colleague Dov Monderer, are a very important class of models in game theory. They have special properties such as the existence of Nash equilibria in pure strategies. This note introduces graphical…
Mathematical models help keep vaccine prices low.
problem Pricing COVID-19 vaccines to ensure affordability and profitability.
method Optimization and game theory approaches modeling a duopoly market.
result Government can negotiate low prices while manufacturers earn profits.
GeoShapley uses game theory to measure spatial effects in ML models.
problem Measuring the impact of location on machine learning model predictions.
method Extends Shapley value framework to quantify spatial effects in various ML models.
result Validated GeoShapley values against known processes and demonstrated utility in house price modeling.
This paper relaxes the common prior assumption in the public and private information game of Morris and Shin (2000, 2004). For the generalized game, where the agent's prior expectations are heterogenous, it derives a sharp condition for the emergence of unique/multiple equilibria. This condition indicates that unique e…
The paper analyzes competition among fund managers using excess logarithmic returns and constructs games to find optimal allocations.
problem Optimal allocation strategies among fund managers considering excess logarithmic returns.
method Constructs both n-player and mean field games to address the competition problem. result The MFE of the MFG represents the limit of n-player game's equilibrium as n approaches infinity. We calculate the dynamics of tax evasion within a multi-agent econophysics model which is adopted from the theory of magnetism and previously has been shown to capture the main characteristics from agent-based based models which build on the standard Allingham and Sandmo approach. In particular, we implement a feedback…
Model shows how social norms and individual ethics affect tax evasion.
problem Effects of social norms and individual ethics on tax evasion.
method Agent-based model with simulations of different tax compliance behaviors.
result Threshold levels in society composition explain tax evasion extent.
Study identifies spatial inequalities in urban services access based on income.
problem Spatial inequalities in urban services and accessibility based on income.
method Multidimensional approach using land use and public transportation data.
result Low-income population has low access to hospitals and cultural centers, while public schools and sports centers have intermediate accessibility.
We demonstrate by mathematical analysis and systematic computer simulations that redistribution can lead to sustainable growth in a society. The human capital dynamics of each agent is described by a stochastic multiplicative process which, in the long run, leads to the destruction of individual human capital and the e…
We study continuous time Bertrand oligopolies in which a small number of firms producing similar goods compete with one another by setting prices. We first analyze a static version of this game in order to better understand the strategies played in the dynamic setting. Within the static game, we characterize the Nash e…
In many developing countries intellectual property infringement and the commerce of pirate goods is an entrepreneurial activity. Digital piracy is very often the only media for having access to music, cinema, books and software. At the same time, bio-prospecting and infringement of indigenous knowledge rights by intern…
Proposes a self-supervised method for generating spatial audio from monaural audio and video.
problem Generating spatial audio from monaural audio and video recordings is challenging and expensive.
method Uses a self-supervised network with an auxiliary classifier to classify video channels and generate spatial audio.
result The proposed method effectively generates spatial audio from monaural audio and video.
The study develops methods to summarize team passing strategies from soccer data.
problem Modeling spatial passing networks across multiple games with varying positions.
method Multiresolution tensor decomposition and Poisson nonnegative block term decomposition.
result Automatic production of network motifs at different levels of detail.
A game theory study examines gradual concessions in variable contribution games under uncertainty.
problem Gradualism in contribution games due to free rider effect.
method Stochastic game analysis of variable contribution games, extending Nerlove-Arrow model.
result Equilibrium characterized by regular control strategies leading to gradual concession.
SIL learns to repeat past good decisions for better exploration.
problem Improving exploration in reinforcement learning environments.
method Self-Imitation Learning (SIL) is a simple off-policy actor-critic algorithm.
result SIL significantly improves A2C on Atari games and is competitive with state-of-the-art methods.
This paper shows hedging algorithms improve performance in repeated matrix games.
problem Improving multi-agent learning algorithms in repeated matrix games.
method Develops and experiments with hedging algorithms combining a top-level and a set of basic algorithms.
result Well-selected hedging algorithms outperform previous MAL algorithms on repeated matrix games.
VR game data for P300 BCI with raccoon vs demon stimuli.
problem Developing confidence metrics for P300 BCI.
method Multiclass labeled P300 dataset in VR game context.
result Estimation of model's confidence in stimulus predictions.
Deep learning predicts chess player attention using eye-tracking and game data.
problem Predicting chess player attention during game.
method Convolutional neural networks with skip-layer architecture and autoencoder.
result Deep features from natural images help in predicting saliency maps for chess.
This study analyzes Twitter posts from US west coast libraries.
problem Lack of automated methods to analyze thousands of library tweets.
method Used Twitter API to collect and analyze 138,000 tweets.
result Identified 20 topics in library tweets, categorized into public relations, book, event, training, and social good.
Spatial information is not always necessary for spatio-temporal models.
problem The necessity of including spatial information in spatio-temporal models.
method Comparison of spatial agnostic neural networks with state-of-the-art models on ten datasets.
result Spatial information is not always needed in most spatio-temporal models.
This study analyses, through cross-section estimation methods, the influence of spatial effects in productivity (product per worker), at economic sectors level of the NUTs III of mainland Portugal, from 1995 to 1999 and from 2000 to 2005 (taking in count the data availability and the Portuguese and European context), c…
This study simulates the evolution of artificial economies in order to understand the tax relevance of administrative boundaries in the quality of life of its citizens. The modeling involves the construction of a computational algorithm, which includes citizens, bounded into families; firms and governments; all of them…
DREAM learns optimal strategies in imperfect games without needing a simulator.
problem Learning optimal strategies in imperfect-information games with multiple agents.
method DREAM is a deep reinforcement learning algorithm that converges to Nash Equilibria and coarse correlated equilibria.
result DREAM achieves state-of-the-art performance in benchmark games and is competitive with simulator-based algorithms.
AttriGuard defends against attribute inference attacks via adversarial machine learning.
problem Attribute inference attacks on user data from public data.
method Two phases: finding minimum noise and sampling attribute values.
result AttriGuard outperforms existing methods in protecting user data.
DSF improves EEG model robustness to missing channels and noise.
problem Robust learning from corrupted EEG data with missing channels.
method Dynamic Spatial Filtering (DSF) as a multi-head attention module.
result DSF achieves up to 29.4% accuracy improvement over baseline models in noisy conditions.
Stable cooperation emerges in fluctuating environments.
problem Evolutionary stability of cooperation in fluctuating conditions.
method Agents with fluctuating wealth share public goods, leading to cooperation.
result Agents with cooperation produce an advantage in fluctuating environments.
This paper investigates the relevance of the No-Ponzi game condition for public debt (i.e. the public debt growth rate has to be lower than the real interest rate, a necessary assumption for Ricardian equivalence) and of the transversality condition for the GDP growth rate (i.e. the GDP growth rate has to be lower than…
New algorithm finds optimal sample complexity for pure exploration with multiple good answers.
problem Determining the optimal number of samples needed to explore multiple good answers in a bandit problem.
method Derive lower bound using game equilibrium, extend Track-and-Stop algorithm to multiple answers.
result New algorithm has asymptotic sample complexity matching the derived lower bound.
JPS improves joint policies for multi-agent collaboration in imperfect information games.
problem Learning good joint policies for multi-agent collaboration with imperfect information.
method Decomposes global changes to localized policy changes, iteratively improving joint policies without re-evaluating the entire game.
result JPS improves solutions provided by unilateral approaches and outperforms algorithms designed for collaborative policy learning.
New method reduces spatial graphs while preserving their topological features.
problem Finding a smaller spatial graph with the same structure.
method Topological spatial graph coarsening approach based on triangle-aware graph filtration.
result Significant reduction in graph size while preserving topological information.
We develop a machine learning framework for solving high-dimensional MFG and MFC problems.
problem Solving high-dimensional mean field games and control problems.
method Combining Lagrangian and Eulerian viewpoints, using neural network parameterization, and avoiding spatial discretization.
result Approximate solutions for 100-dimensional optimal transport and crowd motion problems.
Survey of robust clustering methods for hotspot detection.
problem Detecting false positives in spatial hotspot mapping.
method Statistically rigorous clustering techniques.
result Survey of models and algorithms for robust clustering.
Replication confirms CGD's effectiveness in competitive games.
problem Reproducibility of a novel Nash equilibrium algorithm.
method Replicated experiments and provided Python implementation.
result CGD avoids oscillatory and divergent behaviours.
Agent57 beats human average on all Atari games.
problem Atari game benchmark's poor performance in challenging games.
method Trained neural network with adaptive policy selection and novel architecture.
result First deep RL agent to outperform human average on all 57 Atari games.
S-MTGPR improves normative modeling of neuroimaging data.
problem Normative modeling of neuroimaging data without spatial covariance structure.
method Scalable multi-task Gaussian process regression (S-MTGPR) with low-rank approximation and Kronecker product.
result S-MTGPR provides higher sensitivity in novelty detection scenarios.
Two neural network models analyze bus system efficiency and demand.
problem Identify service gaps and quantify demand in public transportation.
method Two neural network models considering demographic data and metrics.
result Models can generalize to other cities' bus systems.
Bayesian model predicts public transport usage during events.
problem Difficulty in predicting transportation disruptions during special events.
method Bayesian additive model with Gaussian process components, using smart card records and web data.
result Model outperforms baselines by up to 26% in R2 and explains individual event components.
Proposes a new contrastive loss for semi-supervised medical image segmentation.
problem Lack of labeled data for medical image segmentation.
method Uses pseudo-labels and a local contrastive loss to learn good local representations.
result Achieved high segmentation performance on public cardiac and prostate datasets.
Simpler CNN model with spatial attention and temporal pooling outperforms complex models.
problem Emotion recognition from videos with small face deformations and identity variations.
method Spatial attention mechanism and temporal softmax pooling applied to a pre-trained CNN.
result The approach achieves higher accuracy than state-of-the-art methods on the EmotiW dataset.
We consider learning, from strictly behavioral data, the structure and parameters of linear influence games (LIGs), a class of parametric graphical games introduced by Irfan and Ortiz (2014). LIGs facilitate causal strategic inference (CSI): Making inferences from causal interventions on stable behavior in strategic se…
Game theory shows miners' hardware improvements don't centralize mining.
problem Decentralization of cryptocurrency mining.
method Game-theoretical model of mining efficiency and competition.
result Advancements in mining hardware efficiency do not lead to centralization.
Study analyzes global public sentiment on DeFi from 2012-2022.
problem Global public sentiment on DeFi is understudied.
method Sentiment analysis, spatial econometrics, clustering, topic modeling.
result Economic development significantly influences DeFi engagement, especially after 2015.
3D good continuation model explains stereo vision using neurogeometry.
problem Understanding how the brain processes 3D visual correspondence.
method Developed a neurogeometric model involving spatial and orientation disparities.
result Provides insight into neural organization and correspondence problem.
New method for estimating spatial associations with discrete data, even under model misspecification.
problem Estimating associations between covariates and discrete responses with spatial variability and nonrandom sampling.
method Proposes a novel approach to handle spatially varying noise, provides a proof of consistency, and uses a delta method argument.
result Empirically shows reliable confidence intervals compared to standard methods, even with model misspecification.