Research analyzes Georgia's tax system and suggests improvements.
problem Improving tax revenues in Georgia's state budget.
method Analysis of past and present tax systems, review of influencing factors, comparison of foreign models.
result Stimulating measures can increase tax revenues for Georgia's state budget.
Study on tax administration issues and their impact on Georgia's budget revenues.
problem Problems in revenue administration and tax rates in Georgia.
method Analyzed foreign experience and proposed a progressive tax system.
result A progressive tax system would benefit Georgia's business and economy.
'There is no terror in the bang, only is the anticipation of it' - Alfred Hitchcock. Yet there is everything in correctly anticipating the bang a movie would make in the box-office. Movies make a high profile, billion dollar industry and prediction of movie revenue can be very lucrative. Predicted revenues can be used …
Study estimates long-term effects of online advertising mechanisms on user behavior and revenue.
problem Estimating long-term treatment effects in online advertising systems.
method Modeling treatment effects as a stopped random walk, using experimental design, and statistical methods.
result Confidence intervals for long-term treatment effects constructed using statistical techniques.
The paper introduces revenue uplift modeling to maximize marketing profits.
problem Maximizing incremental sales versus maximizing incremental revenue in marketing campaigns.
method Response transformation and two-stage models to decompose campaign profit.
result Revenue uplift modeling can improve campaign profit substantially.
This paper optimizes multi-channel sequential advertising to maximize cumulative revenue.
problem Maximizing cumulative revenue in multi-channel sequential advertising under a budget constraint.
method Formulated as a dynamic knapsack problem, proposed a bilevel optimization framework with action space reduction.
result Significantly improved cumulative revenue compared to state-of-the-art baselines.
Online advertising in E-commerce platforms provides sellers an opportunity to achieve potential audiences with different target goals. Ad serving systems (like display and search advertising systems) that assign ads to pages should satisfy objectives such as plenty of audience for branding advertisers, clicks or conver…
Study on revenue management with limited switches, achieving strong performance and reduced switch counts.
problem Resource-constrained dynamic pricing with limited switching constraints.
method Developed algorithms for blind network revenue management and bandits with knapsacks, achieving optimal regret rates.
result Optimal regret rates are fully characterized by a piecewise-constant function of the switching budget and resource constraints.
Georgia needs a new budget code to manage fiscal policies effectively.
problem Weak and incomplete law on Budget System hinders fiscal policy implementation.
method Develop and adopt a new Budget Code with equal force as the Tax Code.
result Effective correlation between state, regional, and local budgets is crucial for social-economic development.
This paper proposes a new AED framework for multi-metric experiments with fixed budget.
problem Statistical power challenges in testing multiple metrics simultaneously.
method Two-phase structure: adaptive exploration followed by validation. SHRVar algorithm with relative-variance-based sampling.
result Achieves provable error probability that decreases exponentially.
Proposes a greedy algorithm for telecom offers to retain subscribers.
problem Maximizing revenue while preventing churn in telecom subscribers.
method Combinatorial algorithm for offer optimization under heterogeneous incentives.
result Efficient and accurate solution for large subscriber bases.
Solves online resource allocation problems with budget constraints.
problem Maximizing revenue for e-commerce platforms under budget constraints.
method Integrated online optimization and learning algorithm for non-stationary Poisson processes.
result Effective and efficient solutions for constrained resource allocation problems.
We analyze annual revenues and earnings data for the 500 largest-revenue U.S. companies during the period 1954-2007. We find that mean year profits are proportional to mean year revenues, exception made for few anomalous years, from which we postulate a linear relation between company expected mean profit and revenue. …
Study shows local governments smooth fiscal shocks from property tax revenues.
problem Impact of revenue shocks on local fiscal policy.
method Causal machine learning strategies and post-double-selection LASSO estimator.
result Local policymakers predominantly smooth fiscal shocks, but react differently to positive and negative shocks.
This paper optimizes revenue and resource balance in network revenue management.
problem Maximizing revenue while ensuring fair resource consumption across different suppliers.
method Introduces a regularized revenue objective and a primal-dual UCB algorithm for continuous prices and balancing.
result Achieves a worst-case regret of O ~ ( N 5 / 2 T ) \widetilde O(N^{5/2}\sqrt{T}) O ( N 5/2 T ) for revenue maximization and balancing. Paper forecasts tax revenues in Bulgaria during pandemic.
problem Forecasting tax revenues during pandemic.
method Model based on IMF recommendations, using 1995-2019 data.
result Pandemic negatively impacts tax revenues, but econometrics can still produce forecasts.
A scalable model estimates revenue uncertainty for SMEs.
problem Estimating revenue uncertainty for SMEs to manage credit limits.
method Scalable Natural Gradient Boosting Machines.
result The method distinguishes accurate from inaccurate revenue forecasts.
Germany's tax admin costs likely exceed 20% of total revenue, requiring system improvement.
problem High tax administrative costs in Germany and other jurisdictions.
method Statistical data, surveys, and a novel approach to measure total administrative cost as a percentage of total tax revenue.
result Germany's 2021 tax administrative costs likely exceeded 20% of total tax revenue.
Study finds tax avoidance and IT issues hinder revenue in Gombe state.
problem Problems of personal income tax on revenue generation in Gombe state.
method Survey with primary and secondary data, chi square test.
result Tax avoidance and IT issues are major problems.
The paper proposes a new method for product recommendation that considers revenue contributions and user similarity.
problem High dimensionality and sparsity in user-item data, especially in terms of revenue contributions.
method The approach encodes revenue contributions in the user-item matrix and computes customer similarity using suitable distance measures.
result The method segments users based on revenue-based similarity and supports recommendations aligned with profitability objectives.
Model for dynamic pricing across multiple RE groups to maximize revenue.
problem Maximizing revenue from multiple RE pricing groups.
method Mathematical model incorporating multiple pricing groups, revenue goals, and time value of money.
result Algorithm for constructing a pricing policy for multiple RE groups.
Improved algorithms solve multi-period multi-class packing problems with bandit feedback.
problem Optimizing item packing under budget constraints with class-dependent rewards and bandit feedback.
method Developed a new estimator and a closed-form bandit policy for linear contextual multi-class multi-period packing problems.
result The proposed policy achieves sublinear regret in non-degenerate contexts, significantly outperforming benchmarks.
A blockchain protocol uses bandit algorithms to dynamically price transactions.
problem Maximizing revenue from decentralized blockchain Indexers competing for queries.
method Dynamic pricing using Gaussian bandits for multiple agents.
result Improved revenue through dynamic pricing in a decentralized blockchain environment.
This study improves valuation of post-revenue biopharmaceutical assets using Pfizer's data.
problem Accurate valuation of post-revenue drug assets in biotech and pharma.
method Historical sales data analysis to forecast future sales and calculate Net Present Value.
result Demonstrates a method for more informed investment decisions in biotech and pharma.
Proposes balancing revenue and environmental impact in assortment planning.
problem Maximizing revenue while considering environmental impact in retail assortment planning.
method Multi-objective optimization using Higg Material Sustainability Index.
result Shows it's possible to have lower environmental impact without significant revenue loss.
The study uses historical revenue data to forecast music catalog cashflows and multipliers.
problem Valuation of music catalogs based on historical revenue data.
method Risk-neutral approach using discounted cashflows formula.
result Ask prices are close to multipliers justified by median song cashflows, while best bids are near multipliers justified by bottom decile cashflows.
The paper explores fairness, welfare, and equity in personalized pricing across various applications.
problem Interplay of fairness, welfare, and equity in personalized pricing based on customer features.
method Comprehensive literature review and observational metrics without underlying valuation distribution assumptions.
result Personalized pricing can expand access, improve welfare, and increase revenue or budget utilization.
Optimizes web publisher revenues from RTB auctions.
problem Maximizing revenue from RTB auctions with limited information.
method Incremental time-weighted matrix factorization for user and placement profiles; Aalen's Additive model for censored bid predictions.
result Significant revenue increase for web publishers.
For any business, planning is a continuous process, and typically business-owners focus on making both long-term planning aligned with a particular strategy as well as short-term planning that accommodates the dynamic market situations. An ability to perform an accurate financial forecast is crucial for effective plann…
Proposes a revenue function to evaluate dendrograms from comparisons.
problem Evaluate dendrograms from comparisons without ground-truth.
method Introduces a new revenue function related to Dasgupta's cost.
result Revenue function allows meaningful evaluation of dendrograms.
Study learns optimal auctions from corrupted or perturbed bidder valuation samples.
problem Learning revenue-optimal auctions from corrupted or perturbed samples.
method Proves upper bounds, proposes algorithms for learning near-optimal auctions.
result Proves tight upper bounds and proposes algorithms for near-optimal auctions.
This paper improves bidding price prediction for ancillary services markets, boosting revenues.
problem Volatility in renewable energy sources affects grid stability and revenue optimization.
method Machine learning models (SVR, DT, k-NN) and offset adjustment for pay-as-bid markets.
result The proposed approach increases potential revenues by 27.43% to 37.31% compared to baseline models.
In markets for online advertising, some advertisers pay only when users respond to ads. So publishers estimate ad response rates and multiply by advertiser bids to estimate expected revenue for showing ads. Since these estimates may be inaccurate, the publisher risks not selecting the ad for each ad call that would max…
This work studies learning curves for revenue maximization algorithms.
problem Understanding the performance of revenue-maximizing algorithms as they learn from more data.
method Initiates the study of learning curves for revenue maximization, providing a near-complete characterization of their rate of decay.
result Learning curves for revenue maximization can decay arbitrarily slowly or almost exponentially fast, depending on the distribution and optimal revenue.
Online advertisement is the main source of revenue for Internet business. Advertisers are typically ranked according to a score that takes into account their bids and potential click-through rates(eCTR). Generally, the likelihood that a user clicks on an ad is often modeled by optimizing for the click through rates rat…
A new RL approach optimizes reserve prices in multi-phase auctions, reducing revenue regret.
problem Optimizing reserve prices in multi-phase second-price auctions with noisy and potentially untruthful bidders.
method Combines RL techniques with buffer periods, a novel algorithm, and LSVI-UCB extension.
result Achieves optimal revenue regret under known and unknown noise conditions.
Paper shows re-solving heuristics have constant regret for price-based revenue management.
problem Optimal pricing policies for revenue management with time constraints.
method Proves re-solving heuristics have O ( 1 ) O(1) O ( 1 ) regret compared to optimal policies. result Improved regret bound to O ( 1 ) O(1) O ( 1 ) from O ( ln T ) O(\ln T) O ( ln T ) , complemented by Ω ( ln T ) Ω(\ln T) Ω ( ln T ) gap with fluid model. Simplifies complex pricing models for better interpretability and revenue.
problem Complex pricing models are hard to interpret and not widely adopted.
method Model distillation to create interpretable pricing policies.
result Maximizes revenue while maintaining interpretability.
DeepCausalMMM models marketing impacts using deep learning and causal inference.
problem Traditional MMM approaches struggle with non-linear dynamics and temporal patterns.
method Combines deep learning, causal inference, and marketing science. Uses GRUs for temporal patterns and DAG structure for channel dependencies.
result Captures non-linear dynamics and temporal patterns in marketing impacts.
The paper tackles revenue management with time-varying demand using posterior sampling.
problem Maximizing revenue in real-time applications with unknown and time-varying demand.
method Episodic generalization of RM problem, posterior sampling algorithm for linear programming optimization.
result The proposed algorithm outperforms other methods and is comparable to the optimal policy in hindsight.
New method for probabilistic modeling of integer submodular functions.
problem Lack of probabilistic modeling for integer submodular functions.
method Proposed Generalized Multilinear Extension and block-coordinate ascent algorithm.
result Demonstrated effectiveness and viability on real-world datasets.
OTT services are replacing traditional telecom services, affecting revenue streams.
problem OTT services are reducing traditional telecom revenue streams.
method Quantitative analysis of mobile user trends and revenue changes.
result OTT services are becoming more prevalent and impacting telecom revenue.
Contextual bandit framework improves revenue optimization in securities lending market.
problem Optimizing revenue for agent lenders in a dynamic securities lending market.
method Utilized contextual bandit frameworks to address dynamic pricing problems in an e-commerce-like securities lending market.
result Contextual bandit approach consistently outperforms traditional methods by at least 15% in total revenue generated.
A new RL method improves revenue management with delayed feedback.
problem Delayed feedback in revenue management affects substantial value.
method Choice-model-assisted Q-learning for delayed feedback revenue management.
result Q-learning with model-imputed targets converges to an optimal Q-function.
In recent years, RTB(Real Time Bidding) becomes a popular online advertisement trading method. During the auction, each DSP(Demand Side Platform) is supposed to evaluate current opportunity and respond with an ad and corresponding bid price. It's essential for DSP to find an optimal ad selection and bid price determina…
Triangle fees adjust fees based on trade size and price movement, improving price accuracy and revenue.
problem Price staleness and low fee revenue in AMMs.
method Decreasing marginal fees proportional to price movement, creating incentives for price accuracy.
result Triangle fees strictly improve the Pareto frontier of price accuracy versus losses.
Study examines revenue from scam tokens on Ethereum, revealing key characteristics and market factors.
problem Revenue from scam tokens on Ethereum blockchain.
method Empirical analysis of Uniswap, examining characteristics and market factors.
result Revenue from scam tokens is influenced by market economic factors and community engagement.
Proposes a variational autoencoder for long-term customer revenue forecasting.
problem Predicting long-term customer revenue from sparse and irregular transaction data.
method Variational Autoencoder (VAE) with flexible latent representation.
result Improves upon latest benchmarks in multiple real-world datasets.