Fractal neural networks play SimCity and Conway's Game of Life on varying scales.
problem Generalizing agents' performance to larger gameboards than during training.
method Reinforcement learning in a custom environment, using fractal neural networks.
result Agents can generalize to larger gameboards, solving a minigame unsolvable with local strategies.
Convolutional networks struggle to learn Game of Life, even with lottery ticket weights.
problem Training convolutional networks to predict Conway's Game of Life is challenging.
method Examined small convolutional networks trained on Game of Life, focusing on weight initializations and network sizes.
result Minimal networks require significantly more parameters to converge, and their performance is sensitive to small changes in weights.
DeepRole learns to play hidden role games like Avalon.
problem Learning cooperation in uncertain multi-agent settings.
method Combines CFR and deep learning with deductive reasoning.
result DeepRole outperforms other agents and humans in Avalon.
Size-independent neural transfer for RDDL planning.
problem Sample inefficiency and time-consuming training for neural planners of RDDL MDPs.
method Two key innovations: state encoder and parameter-tied action decoder.
result Powerful transfer across problem sizes with superior learning curves.
The paper adds explanation to predictive process monitoring.
problem Equipping predictive business process monitoring with explanation capabilities.
method Used game theory of Shapley Values to obtain robust explanations.
result First time explanations given in predictive business process monitoring.
A game environment simulates competition among many agents for resources.
problem Understanding large-scale multiagent interactions and resource competition.
method Developed a persistent, massively multiplayer AI environment.
result Population size affects the development of skillful behaviors and niche differentiation.
Volatility smiles emerge from imperfect hedging in financial markets.
problem Imperfect hedging in financial markets leads to volatility smiles.
method Examined option prices as fair game agreements based on expected payoffs and risk.
result Resulting prices lead to the volatility smile.
PredNet excels at natural videos but fails on artificial rules, suggesting a trade-off.
problem Generalization of neural network performance across different datasets.
method Evaluation of PredNet on natural and artificial video datasets.
result PredNet's high performance on natural videos is incompatible with its inability to learn artificial rules.
Constant and symmetric price impact functions, most commonly used in agent-based market modelling, are shown to give rise to paradoxical and inconsistent outcomes in the simplest case of arbitrage exploitation when open-hold-close actions are considered. The solution of the paradox lies in the non-constant nature of re…
A new reward learning module improves imitation learning in high-dimensional environments.
problem Challenges in high-dimensional environments for imitation learning.
method Generative model to generate intrinsic reward signals.
result Our method outperforms state-of-the-art IRL methods on Atari games.
Study of repeated games with unobserved agent rewards using MAB framework.
problem Designing policies for principals in repeated principal-agent games with unobservable agent rewards.
method Developed a policy achieving low regret (square-root regret up to a log factor) for perfect-knowledge agents.
result Constructed an estimator for agent's expected reward and designed a policy achieving low regret.
Reinforcement Learning (RL) is a learning paradigm concerned with learning to control a system so as to maximize an objective over the long term. This approach to learning has received immense interest in recent times and success manifests itself in the form of human-level performance on games like \textit{Go}. While R…
All people have to make risky decisions in everyday life. And we do not know how true they are. But is it possible to mathematically assess the correctness of our choice? This article discusses the model of decision making under risk on the example of project management. This is a game with two players, one of which is…
Understanding the influence of features in machine learning is crucial to interpreting models and selecting the best features for classification. In this work we propose the use of principles from coalitional game theory to reason about importance of features. In particular, we propose the use of the Banzhaf power inde…
We determine how an individual can use life insurance to meet a bequest goal. We assume that the individual's consumption is met by an income, such as a pension, life annuity, or Social Security. Then, we consider the wealth that the individual wants to devote towards heirs (separate from any wealth related to the afor…
The paper revisits and applies FTAP to life insurance and annuities pricing.
problem Non-arbitrage pricing of life contingent assets in dynamic markets.
method Revisit FTAP, use martingale theory, apply FTAP to life insurance and annuities, clarify assumptions.
result Valuation formula for life contingent assets including life insurance policies and annuities.
Humans are capable of attributing latent mental contents such as beliefs or intentions to others. The social skill is critical in daily life for reasoning about the potential consequences of others' behaviors so as to plan ahead. It is known that humans use such reasoning ability recursively by considering what others …
The study examines how different interpolation methods affect the decomposition of life insurance surplus.
problem The impact of different interpolation methods on the decomposition of life insurance surplus.
method The study uses the IASU decomposition method to analyze the effects of different interpolation methods (Lee-Carter and linear) on the surplus decomposition.
result Lee-Carter and linear interpolation yield almost identical decompositions, while constant approximations result in different decompositions.
Novel approach models life events using causal discovery and survival analysis.
problem Modeling life event choices and occurrence from a probabilistic perspective.
method Bi-level problem formulation: causal discovery for life events graph, survival analysis for time-to-event modeling.
result Identification of causal relationships and factors influencing transition rates between life events.
Bayesian MS-VAR model for pricing equity-linked life insurance products.
problem Pricing and hedging equity-linked life insurance products on maximum of several assets.
method Introduces Bayesian Markov-Switching Vector Autoregressive (MS-VAR) process to model economic variables and insured's lifetime.
result Obtains net single premiums and hedging formulas for equity-linked life insurance products.
In this paper, we study a stochastic optimal control problem with stochastic volatility. We prove the sufficient and necessary maximum principle for the proposed problem. Then we apply the results to solve an investment, consumption and life insurance problem with stochastic volatility, that is, we consider a wage earn…
Optimizes capital structure for life insurance companies with surplus participation.
problem Determining the optimal participation rate in life insurance contracts.
method Adapted Leland's dynamic capital structure model to life insurance context.
result Optimal participation rate is highly sensitive to contract duration and tax rate.
Investigates optimal life insurance and annuity decisions in inflationary economies.
problem Optimal consumption and investment decisions in an inflationary economy with money illusion.
method Formulated as a random horizon utility maximization problem, derived optimal strategy.
result Money illusion increases life insurance demand for young adults and reduces annuity demand for retirees.
Two-dimensional transition rates improve life insurance reserve calculations.
problem Calculating life insurance reserves with Markov assumptions.
method Introducing two-dimensional forward and backward transition rates.
result Two-dimensional transition rates enable more accurate reserve calculations.
This text is intended to become in the long run Chapter 3 of our long saga dedicated to Riemann, Ahlfors and Rohlin. Yet, as its contents evolved as mostly independent (due to our inaptitude to interconnect both trends as strongly as we wished), it seemed preferable to publish it separately. More factually, our account…
A competition increases financial transaction models' robustness against attacks.
problem Neural networks used by banks are vulnerable to adversarial attacks in financial transaction data.
method A novel competition where participants propose attacks and defenses, simulating real-world conditions.
result Participants' strategies and outcomes provide insights into improving financial transaction models' robustness.
This paper explores how machine learning can improve life insurance risk assessment.
problem Limited use of machine learning in life insurance due to statistical models' efficiency.
method Review and extension of traditional actuarial methodologies with machine learning techniques.
result Developed Python library for life insurance data, improving risk modeling.
Study large deviations in life insurance portfolios without identical distributions.
problem Large deviations in life insurance portfolios with bounded losses and variances.
method Upper bound from standard large deviations, counterexample for full large deviation principle.
result Exponential bound for average loss exceeding a threshold.
For surfaces, we brush a reasonably sharp picture of the influence of the fundamental group upon the complexity of foliated-dynamics. A metaphor emerges with phase-changes through the solid-liquid-gaseous states. Groups of ranks 0≤r≤1 are frozen with intransitivity reigning ubiquitously. When 2≤r≤3, th…
LIFE framework improves model accuracy and interpretability.
problem Achieving high prediction accuracy and interpretability in neural networks.
method Three-step process: subset definition, feature creation, and linear model combination.
result LIFE consistently outperforms other models in prediction accuracy and interpretability.
Investigates timing and asset allocation for life insurance in uncertain financial planning.
problem Optimal timing and asset allocation for life insurance in uncertain financial planning.
method Analytical solutions using duality theory and free-boundary problems.
result Explicit expressions for value functions and optimal strategies in both scenarios.
New formulas estimate life insurance benefits with less computation.
problem Estimating future discretionary benefits in life insurance.
method Derive analytic formulas for lower and upper bounds of FDB.
result Simple estimator for FDB with average of lower and upper bounds.
Analyzes incentives and strategies in financial networks.
problem Deciding default status and liabilities in a network of banks.
method Refined model of financial systems with priority assignments.
result Actions by banks can influence their own outcomes.
We determine the optimal strategies for purchasing term life insurance and for investing in a risky financial market in order to maximize the probability of reaching a bequest goal while consuming from an investment account. We extend Bayraktar and Young (2015) by allowing the individual to purchase term life insurance…
The paper optimizes investment strategies with constraints for life-cycle models.
problem Maximizing consumption, death benefit, and wealth under trading constraints.
method Deep pricing kernel approach to solve constrained portfolio optimization.
result Individuals reduce consumption, insurance demand, and wealth due to constraints.
Paper presents content-based models for game recommendation in cold start scenarios.
problem Cold start problem in game recommendation where new games and players have no historical data.
method Uses survey data to develop content-based interaction models that generalize to new games, players, and both.
result Content models outperform collaborative filtering in predicting new interactions.
Reinsurance can help life insurers maintain higher capital guarantees without losing utility.
problem Decreasing capital guarantees in life insurance products.
method Dynamic investment-reinsurance optimization problem with simultaneous Value-at-Risk and no-short-selling constraints. Introduced guarantee-equivalent utility gain for comparison.
result Optimally managed reinsurance allows insurers to offer higher capital guarantees without reducing expected utility.
In this work, we ask the following question: Can visual analogies, learned in an unsupervised way, be used in order to transfer knowledge between pairs of games and even play one game using an agent trained for another game? We attempt to answer this research question by creating visual analogies between a pair of game…
Deep RL drone trained to compete against classical path planning in drone racing.
problem Optimizing long-term drone racing strategies using reinforcement learning.
method Used PPO algorithm on a simulated quadrotor in a racing environment created with AirSim.
result Deep RL agent outperformed classical path planning in drone racing competitions.
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…
We obtain various estimates of the life-time of two-dimensional minimal tubes in R^3 by potential theory methods.
A new framework for playing and learning board games.
problem Tackling the tedious and repetitive aspects of coding for board game AI.
method Developed a generic TD(λ)-n-tuple agent for arbitrary board games. result TD(λ)-n-tuple outperforms other generic agents on various games. IGGP learns game rules from varying quality game play, finding no overall trend.
problem Learn game rules from varying quality game play.
method Used Sancho's intelligent game traces and ILP systems (Metagol, Aleph, ILASP) to induce game rules from traces of varying quality and volume.
result No overall trend in accuracy of learned game rules from varying quality and volume of training data.
The article proposes a dynamic model for a company's life cycle under competitive influence.
problem Modeling a company's life cycle in a competitive environment.
method Utilized Markov model with known action costs and transition probabilities, affected by outside factors.
result Demonstrates the usefulness of the model in determining future actions of a company.
We consider the problem of how an individual can use term life insurance to maximize the probability of reaching a given bequest goal, an important problem in financial planning. We assume that the individual buys instantaneous term life insurance with a premium payable continuously. By contrast with Bayraktar et al. (…
Adapts concordance probability for large non-life insurance datasets.
problem Capturing discriminatory ability in large non-life insurance datasets.
method Adapts C-index definition and presents two estimation procedures.
result Validates the new procedures for various versions of C-index.
In this paper we investigate the life-span of classical solutions to the hyperbolic geometric flow in two space variables with slow decay initial data. By establishing some new estimates on the solutions of linear wave equations in two space variables, we give a lower bound of the life-span of classical solutions to th…
A game on diagrams switches crossing directions to achieve connectedness.
problem Achieving connectedness in diagrams through crossing switches.
method Players switch crossing directions on regions of a diagram to achieve connectedness.
result Connectedness can be achieved through strategic crossing switches.