This paper simplifies the Nash Bargaining Solution for use in intellectual property cases.
problem Limited application of Nash Bargaining Solution in assigning intellectual property damages.
method Normalizes the Nash Bargaining Solution and provides a methodology for determining bargaining weight.
result Clarifies the application of Nash Bargaining Solution to specific case facts.
A new approach to group fairness treats it as a bargaining problem.
problem Fairness in deploying predictors across subpopulations.
method Interpreting fairness as a bargaining problem and proposing relative improvement.
result Relative improvement provides axiomatic justification and finite-sample convergence guarantees.
We introduce a strategic behavior in reinsurance bilateral transactions, where agents choose the risk preferences they will appear to have in the transaction. Within a wide class of risk measures, we identify agents' strategic choices to a range of risk aversion coefficients. It is shown that at the strictly beneficial…
Paper refines royalty determination using Bayesian methods.
problem Determining a reasonable royalty with risk and uncertainty.
method Bayesian Cost approach to refine Nash Bargaining Solution.
result Nash Bargaining Solution emerges as more reliable.
The paper tackles fair sharing of exploration costs across groups in online learning.
problem Sharing the cost of exploration fairly across multiple groups in online learning.
method The paper introduces the 'grouped' bandit model and uses axiomatic bargaining theory, specifically the Nash bargaining solution, to formalize fairness.
result The paper derives policies that are optimally fair and regret-optimal, showing that regret-optimal policies can be unfair.
NAMEx merges experts using Nash bargaining for improved performance.
problem Sparse Mixture of Experts merging strategies lack a principled weighting mechanism.
method Reinterpreting expert merging through game theory, introducing Nash Merging and complex momentum.
result NAMEx consistently outperforms competing methods across various tasks and system sizes.
Dual labor market model explains low inflation despite low unemployment.
problem Low inflation despite low unemployment during economic recovery.
method Minimal model of dual labor market to explore factors affecting Phillips curve.
result Changes in bargaining power and labor supply elasticity make Phillips curve flat.
Our work extends Coase's theorem to settings with uncertainty, showing how to maximize social welfare through property rights and learning.
problem Theoretical models of externality often assume perfect knowledge, limiting practical solutions.
method We extend Coase's theorem to a two-player bandit setting with uncertainty, designing a learning policy to maximize social welfare.
result We show that property rights and learning can recover Coase's theorem in settings with uncertainty.
We propose a projected gradient dynamical system as a model for a bargaining scheme for an asset for which the two interested agents have personal valuations which do not initially coincide. The personal valuations are formed using subjective beliefs concerning the future states of the world and the reservation prices …
The article improves the display of acceptable exchange ratios for merging companies.
problem Determining feasible exchange ratios for merging companies.
method Exploits a diagrammatic approach to display the bargaining region.
result Shares face upper and lower bounds for acceptable exchange ratios.
Proposes a value-oriented forecast reconciliation method for renewables in electricity markets.
problem Forecast reconciliation overlooks the value of forecasts in decision-making, leading to unfair outcomes.
method Value-oriented forecast reconciliation using a Nash bargaining framework and a primal-dual algorithm for parameter estimation.
result Consistently increases profits for all agents involved in an aggregated wind energy trading problem.
PropFair algorithm ensures fair performance in federated learning.
problem Ensuring fair performance in federated learning for diverse clients.
method PropFair, a novel algorithm based on bargaining games, finds proportionally fair solutions.
result PropFair approximately finds proportional fairness solutions and balances average and worst 10% client performances.
We look at how asset exchange models can be mapped to random iterated function systems (IFS) giving new insights into the dynamics of wealth accumulation in such models. In particular, we focus on the "yard-sale" (winner gets a random fraction of the poorer players wealth) and the "theft-and-fraud" (winner gets a rando…
Improved UCB method for stochastic bandits using distance tuning.
problem Improving performance in stochastic bandit problems.
method Tuning confidence bounds based on bandit distance.
result Empirically shows increased performance compared to existing methods.
We propose a continuum model for the description of buyer and seller dynamics in an Internet market. The relevant variables are the research effort of buyers and the sellers' reputation building process. We show that, if a commercial web-site gives consumers the possibility to rate credibly sellers they bargained with,…
The paper revisits classical competition theory to explain speculative asset price dynamics.
problem Understanding the dynamics of speculative asset prices and their volatility.
method Specialized classical model of competition with reservation prices, incorporating speculation.
result The model explains excess, fat-tailed, and clustered volatility in speculative asset prices.
This article extends, in a stochastic environment, the Yagil (1987) model which establishes, in a deterministic dividend discount model, a range for the exchange ratio in a stock-for-stock merger agreement. Here, we generalize Yagil's work letting both pre- and post-merger dividends grow randomly over time. If Yagil fo…
An artificial general intelligence (AGI) might have an instrumental drive to modify its utility function to improve its ability to cooperate, bargain, promise, threaten, and resist and engage in blackmail. Such an AGI would necessarily have a utility function that was at least partially observable and that was influenc…
The methodology presented provides a quantitative way to characterize investor behavior and price dynamics within a particular asset class and time period. The methodology is applied to a data set consisting of over 250,000 data points of the S&P 100 stocks during 2004-2018. Using a two-way fixed-effects model, we unco…
We study the effect of altruism in two simple asset exchange models: the yard sale model (winner gets a random fraction of the poorer player's wealth) and the theft and fraud model (winner gets a random fraction of the loser's wealth). We also introduce in these models the concept of bargaining efficiency, which makes …
We consider a nonlinear extension of the generalized network flow model, with the flow leaving an arc being an increasing concave function of the flow entering it, as proposed by Truemper and Shigeno. We give a polynomial time combinatorial algorithm for solving corresponding flow maximization problems, finding an epsi…
The paper compares LOCO and Shapley values for feature importance, highlighting their limitations and suggesting improvements.
problem Quantifying feature importance in the presence of feature correlation.
method LOCO and Shapley Values, critiquing their axioms and proposing new measures.
result Shapley values do not eliminate feature correlation, and a modified LOCO is recommended.
AI advances impact asset management, offering new decision-making capabilities.
problem AI's impact on asset management and potential disruption.
method Analyzing how AI capabilities (reading, reasoning, decision-making) can be applied to asset management.
result AI can revolutionize asset management, but risks vary by fund type.
I derive practical formulas for optimal arrangements between sophisticated stock market investors (namely, continuous-time Kelly gamblers or, more generally, CRRA investors) and the brokers who lend them cash for leveraged bets on a high Sharpe asset (i.e. the market portfolio). Rather than, say, the broker posting a m…
Much research has been conducted arguing that tipping points at which complex systems experience phase transitions are difficult to identify. To test the existence of tipping points in financial markets, based on the alternating offer strategic model we propose a network of bargaining agents who mutually either coopera…
New algorithm minimizes FE objectives for synthetic AIF agents.
problem Minimizing Free Energy for synthetic active inference agents.
method Variational message passing on Constrained FFGs.
result Message passing algorithms induce epistemic behavior in AIF agents.
The paper analyzes auction theory and its impact on buyer demographics.
problem Understanding the dynamics of auctions and buyer demographics.
method Analyzing various auction scenarios and their psychological aspects.
result Identified key auction criteria and their influence on buyer groups.
This work examines the effects of allowing borrowing in betting-based hypothesis testing.
problem The impact of allowing borrowing in betting-based hypothesis testing.
method Examined the consequences of allowing borrowing in each round, adjusting the rejection threshold accordingly.
result There is no extra price to pay for the possibility of borrowing if a path-dependent threshold is used.
New algorithm extracts device profiles for short-term power predictions in commercial buildings.
problem Short-term power prediction in commercial buildings with high accuracy.
method Unsupervised extraction of device profiles from aggregate power measurements, disaggregation using particle swarm optimization, and state changes forecast by artificial neural networks.
result Developed approach outperforms existing methods with high accuracy.
Power quandles improve group invariants and allow group presentations.
problem Understanding and classifying groups using algebraic structures.
method Introducing power quandles, which retain conjugation and power maps, and showing their effectiveness in group theory.
result Power quandles determine central quotients and centers of groups, and can approximate any group.
Novel power transform unifies various mathematical functions.
problem Normalizing and standardizing datasets.
method Presented a novel power transform.
result Unified various mathematical functions.
Study of metrics on positive-definite matrices from power potential, linking to power means.
problem Understanding metrics on positive-definite matrices derived from power potential.
method Explicit expressions for geodesics and distance function derived from Hessian of power potential.
result Geodesics and distance function converge to weighted matrix geometric mean as β tends to zero.
The paper establishes conditions for strict power concavity in convolutions.
problem Conditions for strict power concavity in convolutions.
method Analyzes sufficient conditions for strict parabolic power concavity of convolutions.
result Establishes sufficient conditions for strict power concavity of convolutions.
Power system studies require the topological structures of real-world power networks; however, such data is confidential due to important security concerns. Thus, power grid synthesis (PGS), i.e., creating realistic power grids that imitate actual power networks, has gained significant attention. In this letter, we cas…
Power laws detected in financial data, modeled with random multipliers.
problem Detecting power laws in financial data.
method Investigated data from financial instruments, proposed a model based on sums of Maxwell-Boltzmann distributions with random multipliers.
result Detected power laws with various exponents in financial data, proposed a universal model.
Bayesian method models multivalued power data from wind farms.
problem Accurate modeling of power curves with multivalued relationships.
method Overlapping mixture of probabilistic regression models.
result Model accurately represents practical power data.
WindDragon forecasts wind power with deep learning.
problem Accurate short-term wind power forecasting is crucial for grid operation.
method Automated Deep Learning combined with Numerical Weather Predictions.
result Automated Deep Learning improves wind power forecasting accuracy.
Paper examines power consumption in neural networks using various activation functions.
problem Power consumption in machine learning models.
method Examines power consumption for different activation functions.
result Substantial differences in power consumption exist between activation functions.
Paper introduces reinforcement learning for managing power grids.
problem Balancing power flows and maintaining grid stability in real-time.
method Reinforcement Learning applied to power network operations.
result Demonstrates feasibility of machine learning in power grid management.
Study of origamis' singularities for groups of prime-power order.
problem Classifying singularities of origamis for groups of prime-power order.
method Geometric and group-theoretic ideas used to classify strata.
result Many groups of prime-power order have only one stratum, but some do not.
Photovoltaic systems have been widely deployed in recent times to meet the increased electricity demand as an environmental-friendly energy source. The major challenge for integrating photovoltaic systems in power systems is the unpredictability of the solar power generated. In this paper, we analyze the impact of havi…
Auto-regressive conditionally heteroskedastic (ARCH) family models are still used, by practitioners in business and economic policy making, as a conditional volatility forecasting models. Furthermore ARCH models still are attracting an interest of the researchers. In this contribution we consider the well known GARCH(1…
Survey on GNNs' power and limitations.
problem Theoretical limitations of GNNs.
method Comprehensive overview of GNNs and their variants.
result Provably powerful variants of GNNs.
GP CC-OPF solves uncertain power grid optimization with Gaussian Process.
problem Uncertainty in power grid operations due to high renewables integration.
method Data-driven Gaussian Process regression for solving non-convex CC-OPF problem.
result Effective economic dispatch optimization in uncertain power grids.
New SDE model from machine learning optimization with unique stationary distribution.
problem Stationary distribution of machine learning optimization models.
method Proved ergodicity and unique stationary distribution of power-law dynamic SDE.
result Power-law dynamic has a unique stationary distribution and is ergodic.
Survey of RL methods for optimizing power grid topologies.
problem Optimizing power grid operation with adaptive control strategies.
method Reinforcement Learning (RL) for dynamic and uncertain environments.
result Comprehensive evaluation of RL-based methods for power grid topology optimization.
Sharp analysis of power iteration for tensor PCA, improving convergence and stopping criteria.
problem Analyzing the power iteration algorithm for tensor PCA to improve convergence and stopping criteria.
method Sharp bounds on the number of iterations, revealing a smaller algorithmic threshold, proposing a stopping criterion.
result Sharp bounds on the number of iterations required for power method to converge, revealing a smaller algorithmic threshold than previously conjectured.
The abstract discusses parallels between Galois theory and Stone-Weierstrass theorem in various fields.
problem Connecting distinguishing power and expressive power in different fields.
method Elementary theorem connecting distinguishing power and expressive power.
result Foundational principle in linguistics linking distinguishing power and expressive power.