Stochastic Delay Differential Games: Financial Modeling and Machine Learning Algorithms

In this paper, we propose a numerical methodology for finding the closed-loop Nash equilibrium of stochastic delay differential games through deep learning. These games are prevalent in finance and economics where multi-agent interaction and delayed effects are often desired features in a model, but are introduced at the expense of increased dimensionality of the problem. This increased dimensionality is especially significant as that arising from the number of players is coupled with the potential infinite dimensionality caused by the delay. Our approach involves parameterizing the controls of each player using distinct recurrent neural networks. These recurrent neural network-based controls are then trained using a modified version of Brown's fictitious play, incorporating deep learning techniques. To evaluate the effectiveness of our methodology, we test it on finance-related problems with known solutions. Furthermore, we also develop new problems and derive their analytical Nash equilibrium solutions, which serve as additional benchmarks for assessing the performance of our proposed deep learning approach.Comment: 29 pages, 8 figure

An Open Letter to Congressman Gingrich

We urge you to reconsider your proposal to amend the House Rules to require a three-fifths vote for enactment of laws that increase income taxes. This proposal violates the explicit intentions of the Framers. It is inconsistent with the Constitution\u27s language and structure. It departs sharply from traditional congressional practice. It may generate constitutional litigation that will encourage Supreme Court intervention in an area best left to responsible congressional decision. Unless the proposal is withdrawn now, it will serve as an unfortunate precedent for the proliferation of supermajority rules on a host of different subjects in the future. Over time, we will see the continuing erosion of our central constitutional commitments to majority rule and deliberative democracy

Wrong Turn in Cyberspace: Using ICANN to Route Around the APA and the Constitution

The Internet relies on an underlying centralized hierarchy built into the domain name system (DNS) to control the routing for the vast majority of Internet traffic. At its heart is a single data file, known as the root. Control of the root provides singular power in cyberspace. This Article first describes how the United States government found itself in control of the root. It then describes how, in an attempt to meet concerns that the United States could so dominate an Internet chokepoint, the U. S. Department of Commerce (DoC) summoned into being the Internet Corporation for Assigned Names and Numbers (ICANN), a formally private nonprofit California corporation. DoC then signed contracts with ICANN in order to clothe it with most of the U. S. government\u27s power over the DNS, and convinced other parties to recognize ICANN\u27s authority. ICANN then took regulatory actions that the U. S. Department of Commerce was unable or unwilling to make itself, including the imposition on all registrants of Internet addresses of an idiosyncratic set of arbitration rules and procedures that benefit third-party trademark holders. Professor Froomkin then argues that the use of ICANN to regulate in the stead of an executive agency violates fundamental values and policies designed to ensure democratic control over the use of government power, and sets a precedent that risks being expanded into other regulatory activities. He argues that DoC\u27s use of ICANN to make rules either violates the APA\u27s requirement for notice and comment in rulemaking and judicial review, or it violates the Constitution\u27s nondelegation doctrine. Professor Froomkin reviews possible alternatives to ICANN, and ultimately proposes a decentralized structure in which the namespace of the DNS is spread out over a transnational group of policy partners with DoC

Machine Learning Methods for Stochastic Differential Games and those with Delay: Applications and Modeling in Epidemiology and Finance

Stochastic differential games and those with delay play a crucial role in modeling complex, real-world phenomena. The ability to find Nash equilibria in these games enhances the predictive capabilities of scientists and professionals across various fields and informs optimal decision-making processes. These problems can be computationally demanding to solve, especially in the case of delayed dynamics and interaction among a large number of agents. This dissertation begins with an overview of stochastic differential games and existing machine learning methodologies designed to find their Nash equilibria. We then extend these existing methodologies to the challenging case of stochastic delay differential games with a new algorithm for finding their closed-loop Nash equilibria. To evaluate the effectiveness of our proposed algorithm, we test it on problems with known solutions. In particular, we introduce new financial models based on competing portfolio managers taking into consideration delayed tax-effects. We derive analytical Nash equilibrium solutions for these newly introduced stochastic delay differential games, serving as additional benchmarks to assess the performance of our proposed machine learning approach.Finally, building on the existing machine learning methodologies for stochastic differential games, we introduce a new modified algorithm that we use to solve the Nash equilibrium problem for a game-theoretic, stochastic SEIR (Susceptible-Exposed-Infectious-Recovered) model applied to the COVID-19 pandemic. Solving this proposed model demonstrates the effects of differing policies on the spread of disease over different regions and how these policies affect each other, illustrating the practical effectiveness of the proposed numerical approach

Managing human resources

+682hlm.;27c

Managing Human Resources

xx iii 586 hal 30 c

Managing Human Resources

677hal.;xxxviii.;28c

Management : people, performance, change

xl, 836 p. : ill. (some col.) ; 27 cm