This paper shows how we can build a model for transactions when goods are given away in the expectation of a later settlement. In settings where people keep track of their social accounts we are able to redefine concepts like account balance, yield curve and the law of diminishing returns. The model provides us with a …
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48 results for “social balance”
Using a method rooted in information theory, we present results that have identified a large set of stocks for which social media can be informative regarding financial volatility. By clustering stocks based on the joint feature sets of social and financial variables, our research provides an important contribution by …
Social Security and other public policies can be viewed as a series of cash in and outflows that depend on parameters such as the age distribution of the population and the retirement age. Given forecasts of these parameters, policies can be designed to be financially stable, i.e., to terminate with a zero balance. If …
The paper explores fair machine learning policies for balancing competing objectives in noisy data.
problem Balancing competing objectives in noisy data.
method Analyzes a class of policies that trace an empirical Pareto frontier based on learned scores.
result Characterizes optimal strategies and bounds Pareto errors due to score inaccuracies.
Recent successes in word embedding and document embedding have motivated researchers to explore similar representations for networks and to use such representations for tasks such as edge prediction, node label prediction, and community detection. Such network embedding methods are largely focused on finding distribute…
Optimizes lockdown strategies to balance economic activities and virus spread.
problem Balancing economic activities and virus spread during lockdowns.
method Modeling the pandemic as SEIR, applying Granovetter threshold model for social distancing, and using NSGA-II optimization.
result Optimal lockdown policies for ten weeks to minimize infections and economic impact.
Islamophobic hate speech on social media inflicts considerable harm on both targeted individuals and wider society, and also risks reputational damage for the host platforms. Accordingly, there is a pressing need for robust tools to detect and classify Islamophobic hate speech at scale. Previous research has largely ap…
Novel GNN method for semi-supervised clustering of signed networks.
problem Lack of effective node embeddings for signed network clustering.
method SSSNET: Probabilistic balanced normalized cut loss for GNN.
result SSSNET achieves comparable or better results than state-of-the-art methods.
Computational swarm intelligence consists of multiple artificial simple agents exchanging information while exploring a search space. Despite a rich literature in the field, with works improving old approaches and proposing new ones, the mechanism by which complex behavior emerges in these systems is still not well und…
Proposes a model to optimize feedback for content creators on social media.
problem Nurturing content creation on social media platforms.
method Modeling feedback distribution to incentivize creators and optimize newsfeed experience.
result Optimized newsfeed for content creators without compromising user experience.
Fine-tuned open-source LLMs match or exceed closed-source models in social science research.
problem Limited scalability and high costs of large LLMs in social science research.
method Fine-tuning open-source models for specific tasks, exploring training set size effects, proposing hybrid workflow.
result Small, fine-tuned open-source LLMs achieve equal or superior performance to commercial alternatives.
Quantum computing optimizes ESG portfolios efficiently.
problem Optimizing investment portfolios with risk, return, and ESG considerations.
method Formulated discrete Markowitz portfolio theory (DMPT) for quantum annealers, incorporating ESG ratings.
result Discrete portfolios converge to continuous solutions as budgets increase, outperforming traditional methods.
Motivated by social balance theory, we develop a theory of link classification in signed networks using the correlation clustering index as measure of label regularity. We derive learning bounds in terms of correlation clustering within three fundamental transductive learning settings: online, batch and active. Our mai…
Improves group fairness in tensor completion by augmenting tensors with balanced entities.
problem Preventing discrimination in tensor decomposition based on social grounds.
method STAFF (Sparse Tensor Augmentation For Fairness) which augments tensors with balanced entities to mitigate imbalance and bias.
result Consistently shows the best trade-off between completion error and group fairness, reducing errors by 36% and 59% respectively.
Urban transformations within large and growing metropolitan areas often generate critical dynamics affecting social interactions, transport connectivity and income flow distribution. We develop a statistical-mechanical model of urban transformations, exemplified for Greater Sydney, and derive a thermodynamic descriptio…
Graph machine learning lacks a balanced theory, focusing on expressive power and optimization.
problem Insufficient theoretical understanding of GNNs' generalization behavior.
method Develop a balanced theory focusing on expressive power, generalization, and optimization.
result Theoretical advancements need to align with practical success in graph machine learning.
In practical machine learning systems, graph based data representation has been widely used in various learning paradigms, ranging from unsupervised clustering to supervised classification. Besides those applications with natural graph or network structure data, such as social network analysis and relational learning, …
This paper argues that the fundamental principle of contemporary financial economics is balanced reciprocity, not the principle of utility maximisation that is important in economics more generally. The argument is developed by analysing the mathematical Fundamental Theory of Asset Pricing with reference to the emergen…
Study tackles RLHF with diverse human feedback, showing limitations and proposing a meta-learning approach.
problem Traditional RLHF fails to balance diverse human preferences.
method Integrates meta-learning and multiple social welfare functions to optimize diverse preferences.
result Establishes sample complexity bounds for optimizing diverse social welfare functions.
Proposes a scalable method for counterfactual prediction using machine learning.
problem De-bias causal estimators with high-dimensional data in observational studies.
method Uses entropy balancing to learn weights minimizing Jensen-Shannon divergence, leading to robust counterfactual predictions.
result Consistent causal estimation if either propensity score or outcome model is correctly specified.
Higher environmental performance linked to more tax avoidance, especially for financially constrained firms.
problem Tax avoidance practices in relation to environmental performance.
method Entropy balancing, propensity score matching, instrumental variable method, Heckman test.
result Higher environmental performance correlates with increased tax avoidance, particularly for financially constrained firms.
Toward a better trade-off between performance and fairness with kernel-based distribution matchingcs.LG
As recent literature has demonstrated how classifiers often carry unintended biases toward some subgroups, deploying machine learned models to users demands careful consideration of the social consequences. How should we address this problem in a real-world system? How should we balance core performance and fairness me…
Why, when, and how fast innovations are adoptedphysics.soc-ph
When the full stock of a new product is quickly sold in a few days or weeks, one has the impression that new technologies develop and conquer the market in a very easy way. This may be true for some new technologies, for example the cell phone, but not for others, like the blue-ray. Novelty, usefulness, advertising, pr…
HLRC offers a new curvature metric for hypergraphs that balances interpretability and efficiency.
problem Challenges in geometric characterization of hypergraphs with higher-order interactions.
method Hypergraph lower Ricci curvature (HLRC) defined in closed form.
result HLRC consistently reveals meaningful higher-order organization in diverse hypergraph datasets.
A dataset for detecting online hate speech from YouTube and Reddit comments.
problem Detecting and preventing hate speech on social media platforms.
method Created a dataset with two variants: binary and multi-label, based on YouTube and Reddit comments, using Figure-Eight crowdsourcing platform.
result Demonstrated that even a small amount of labelled data can help detect hate speech occurrences.
Nowadays, users open multiple accounts on social media platforms and e-commerce sites, expressing their personal preferences on different domains. However, users' behaviors change across domains, depending on the content that users interact with, such as movies, music, clothing and retail products. In this paper, we pr…
Study proposes a multi-agent framework to mitigate bias in sentiment analysis.
problem Bias in sentiment analysis models.
method Integrates multiple LLMs, incorporates dialogue sessions, and uses probabilistic prediction.
result KCS+IBC reduces entropy and increases variance, suggesting improved balance between aggregation and diversity.
Regularized spectral methods improve clustering in signed graphs, especially for sparse data.
problem Clustering signed graphs with positive and negative edges.
method Developed regularized versions of SPONGE and Signed Laplacian methods for clustering signed graphs, especially for sparse data.
result Theoretical guarantees and empirical performance improvements for clustering signed graphs, especially in sparse regimes.
Community Detection in Political Twitter Networks using Nonnegative Matrix Factorization Methodscs.SI
Community detection is a fundamental task in social network analysis. In this paper, first we develop an endorsement filtered user connectivity network by utilizing Heider's structural balance theory and certain Twitter triad patterns. Next, we develop three Nonnegative Matrix Factorization frameworks to investigate th…
In [1] we presented a model for transactions when goods are given away in the expectation of a later settlement. In settings where people keep track of their social accounts we were able to redefine concepts like account balance, yield curve and the law of diminishing returns. In this paper we establish a general equil…
Causal Modelling of Cryptocurrency Price Movements Using Discretisation-Aware Bayesian Networksq-fin.ST
Study uses BNs to predict cryptocurrency prices, improving accuracy with discretisation.
problem Predicting price movements in volatile cryptocurrency markets.
method Discretisation-aware Bayesian Networks with three methods and multiple bin counts.
result Equal interval with two bins provides best predictive performance.
Study allocates resources to strategic agents while balancing cost and incentives.
problem Dynamic allocation of reusable resources to strategic agents with private valuations under long-term cost constraints.
method Incentive-aware framework combining epoch-based lazy updates and randomized exploration rounds.
result Achieves social welfare regret, satisfies all cost constraints, and ensures incentive alignment.
Lambda Learner improves model freshness in data streams.
problem Balancing model freshness and computational costs in data streams.
method Incremental updates in response to mini-batches from data streams.
result Lambda Learner outperforms offline models in time-sensitive updates.
PHASE dataset simulates complex social interactions in physical environments.
problem Lack of datasets for evaluating physically grounded perception of complex social interactions.
method Created PHASE dataset of 2D animations with procedural generation and physics engine.
result SIMPLE model outperforms neural networks in recognizing complex social interactions.
A global agreement on how to reduce and cap human footprint, especially their GHG emissions, is very unlikely in near future. At the same time, bilateral agreements would be inefficient because of their neural and balanced nature. Therefore, unilateral actions would have attracted attention as a practical option. Howev…
Improved neural model for social recommendation by integrating social and interest networks.
problem Data sparsity and lack of higher-order relationships in social recommendation.
method DiffNet++ models neural influence diffusion and interest diffusion in a unified framework using a multi-level attention network.
result Extensive experiments on real-world datasets show the effectiveness of DiffNet++.
Social media enhances or diminishes scientific status, depending on usage.
problem Impact of social media on scientific stratification and mobility.
method Logistic Attribution Analysis combining statistical and machine learning methods.
result Social media promotes stratification and mobility, but beyond a threshold, it negatively impacts status.
Bayesian max-margin models have shown superiority in various practical applications, such as text categorization, collaborative prediction, social network link prediction and crowdsourcing, and they conjoin the flexibility of Bayesian modeling and predictive strengths of max-margin learning. However, Monte Carlo sampli…
Social and individual learning in the Minority Gamephysics.soc-ph
Social learning can make financial markets inefficient, but individual learning can fix this.
problem Inefficiencies in financial markets due to social learning.
method Study of the Minority Game model with social and individual learning mechanisms.
result Individual learning can rescue a population from the inefficiencies caused by social learning.
SINN combines social science and deep learning for predicting opinion dynamics.
problem Predicting opinion dynamics in social networks using traditional models requires extensive calibration with real data.
method SINN integrates theoretical models and social media data using physics-informed neural networks (PINNs) and matrix factorization.
result SINN outperforms six baseline methods in predicting opinion dynamics on real-world and synthetic datasets.
Optimizes social interactions for profit, people, and planet using mathematical models.
problem Determining the most effective social configurations for mutual objectives.
method Formulated as a mathematical optimization problem, using (meta)relational models theory.
result Identifies the most suitable combination of sociality forms for mutual objectives.
Model predicts increased social unrest during COVID-19 using social media data.
problem Detecting rising conflict potential in societies during pandemics.
method Neural implicit motive pattern recognition from social media texts.
result Significant increase in conflict indicators during the pandemic.
Enhances traditional MV model for socially responsible investors.
problem Traditional MV models ignore ESG scores relevant to socially responsible investors.
method Implemented an amended MV model considering ESG scores.
result SR investors can achieve competitive SR portfolios with a trade-off between Sharpe Ratio and ESG scores.
The rise in online social networking has brought about a revolution in social relations. However, its effects on offline interactions and its implications for collective well-being are still not clear and are under-investigated. We study the ecology of online and offline interaction in an evolutionary game framework wh…
Survival analysis models predict economic convergence across Americas.
problem Analyzing GDP per capita trajectories and convergence across the Americas.
method Survival analysis, machine learning, economic interpretation.
result DeepSurv captures non-linear interactions in GDP per capita trajectories.
Interdisciplinarity in Socio-economics, mathematical analysis and predictability of complex systemsphysics.soc-ph
In this essay, I attempt to provide supporting evidence as well as some balance for the thesis on `Transforming socio-economics with a new epistemology' presented by Hollingworth and Mueller (2008). First, I review a personal highlight of my own scientific path that illustrates the power of interdisciplinarity as well …
Financial and economic history is strewn with bubbles and crashes, booms and busts, crises and upheavals of all sorts. Understanding the origin of these events is arguably one of the most important problems in economic theory. In this paper, we review recent efforts to include heterogeneities and interactions in models…
Secure social recommendation framework using secret sharing.
problem Privacy concerns and reluctance to share social data in recommender systems.
method Secret Sharing based Matrix Multiplication (SSMM) protocol for secure data sharing and collaborative recommendation.
result SeSoRec framework improves recommendation performance and is secure.