Persuasion, Political Warfare, and Deterrence: Behavioral and Behaviorally Robust Models

This dissertation examines game theory models in the context of persuasion and competition wherein decision-makers are not completely rational by considering two complementary threads of research. The first thread of research pertains to offensive and preemptively defensive behavioral models. Research in this thread makes three notable contributions. First, an offensive modeling framework is created to identify how an entity optimally influences a populace to take a desired course of action. Second, a defensive modeling framework is defined wherein a regulating entity takes action to bound the behavior of multiple adversaries simultaneously attempting to persuade a group of decision-makers. Third, an offensive influence modeling framework under conditions of ambiguity is developed in accordance with historical information limitations, and we demonstrate how it can be used to select a robust course of action on a specific, data-driven use case. The second thread of research pertains to behavioral and behaviorally robust approaches to deterrence. Research in this thread makes two notable contributions. First, we demonstrate the alternative insights behavioral game theory generates for the analysis of classic deterrence games, and explicate the rich analysis generated from its combined use with standard equilibrium models. Second, we define behaviorally robust models for an agent to use in a normal form game under varying forms of uncertainty in order to inform deterrence policy decisions

On Proportionate and Truthful International Alliance Contributions: An Analysis of Incentive Compatible Cost Sharing Mechanisms to Burden Sharing

Burden sharing within an international alliance is a contentious topic, especially in the current geopolitical environment, that in practice is generally imposed by a central authority\u27s perception of its members\u27 abilities to contribute. Instead, we propose a cost sharing mechanism such that burden shares are allocated to nations based on their honest declarations of the alliance\u27s worth. Specifically, we develop a set of multiobjective nonlinear optimization problem formulations that respectively impose Bayesian Incentive Compatible (BIC), Strategyproof (SP), and Group Strategyproof (GSP) mechanisms based on probabilistic inspection efforts and deception penalties that are budget balanced and in the core. Any feasible solution to these problems corresponds to a single stage Bayesian stochastic game wherein a collectively honest declaration is a Bayes-Nash equilibrium, a Nash Equilibrium in dominant strategies, or a collusion resistant Nash equilibrium, respectively, but the optimal solution considers the alliance\u27s central authority preferences. Each formulation is shown to be a nonconvex optimization problem. The solution quality and computational effort required for three heuristic algorithms as well as the BARON global solver are analyzed to determine the superlative solution methodology for each problem. The Pareto fronts associated with each multiobjective optimization problem are examined to determine the tradeoff between inspection frequency and penalty severity required to obtain truthfulness under stronger assumptions. Memory limitations are examined to ascertain the size of alliances for which the proposed methodology can be utilized. Finally, a full block design experiment considering the clustering of available alliance valuations and the member nations\u27 probability distributions therein is executed on an intermediate-sized alliance motivated by the South American alliance UNASUR

Effective Labor Regulation and Microeconomic Flexibility

Microeconomic flexibility, by facilitating the process of creative-destruction, is at the core of economic growth in modern market economies. The main reason for why this process is not infinitely fast, is the presence of adjustment costs, some of them technological, other institutional. Chief among the latter is labor market regulation. While few economists would object to such a view, its empirical support is rather weak. In this paper we revisit this hypothesis and find strong evidence for it. We use a new sectoral panel for 60 countries and a methodology suitable for such a panel. We find that job security regulation clearly hampers the creative-destruction process, especially in countries where regulations are likely to be enforced. Moving from the 20th to the 80th percentile in job security, in countries with strong rule of law, cuts the annual speed of adjustment to shocks by a third while shaving off about one percent from annual productivity growth. The same movement has negligible effects in countries with weak rule of law.

The Infrared Database of Extragalactic Observables from Spitzer I: the redshift catalog

This is the first of a series of papers on the Infrared Database of Extragalactic Observables from Spitzer (IDEOS). In this work we describe the identification of optical counterparts of the infrared sources detected in Spitzer Infrared Spectrograph (IRS) observations, and the acquisition and validation of redshifts. The IDEOS sample includes all the spectra from the Cornell Atlas of Spitzer/IRS Sources (CASSIS) of galaxies beyond the Local Group. Optical counterparts were identified from correlation of the extraction coordinates with the NASA Extragalactic Database (NED). To confirm the optical association and validate NED redshifts, we measure redshifts with unprecedented accuracy on the IRS spectra ({\sigma}(dz/(1+z))=0.0011) by using an improved version of the maximum combined pseudo-likelihood method (MCPL). We perform a multi-stage verification of redshifts that considers alternate NED redshifts, the MCPL redshift, and visual inspection of the IRS spectrum. The statistics is as follows: the IDEOS sample contains 3361 galaxies at redshift 0<z<6.42 (mean: 0.48, median: 0.14). We confirm the default NED redshift for 2429 sources and identify 124 with incorrect NED redshifts. We obtain IRS-based redshifts for 568 IDEOS sources without optical spectroscopic redshifts, including 228 with no previous redshift measurements. We provide the entire IDEOS redshift catalog in machine-readable formats. The catalog condenses our compilation and verification effort, and includes our final evaluation on the most likely redshift for each source, its origin, and reliability estimates.Comment: 11 pages, 6 figures, 1 table. Accepted for publication in MNRAS. Full redshift table in machine-readable format available at http://ideos.astro.cornell.edu/redshifts.htm

On large language models in national security applications

The overwhelming success of GPT-4 in early 2023 highlighted the transformative potential of large language models (LLMs) across various sectors, including national security. This article explores the implications of LLM integration within national security contexts, analyzing their potential to revolutionize information processing, decision-making, and operational efficiency. Whereas LLMs offer substantial benefits, such as automating tasks and enhancing data analysis, they also pose significant risks, including hallucinations, data privacy concerns, and vulnerability to adversarial attacks. Through their coupling with decision-theoretic principles and Bayesian reasoning, LLMs can significantly improve decision-making processes within national security organizations. Namely, LLMs can facilitate the transition from data to actionable decisions, enabling decision-makers to quickly receive and distill available information with less manpower. Current applications within the US Department of Defense and beyond are explored, e.g., the USAF\u27s use of LLMs for wargaming and automatic summarization, that illustrate their potential to streamline operations and support decision-making. However, these applications necessitate rigorous safeguards to ensure accuracy and reliability. The broader implications of LLM integration extend to strategic planning, international relations, and the broader geopolitical landscape, with adversarial nations leveraging LLMs for disinformation and cyber operations, emphasizing the need for robust countermeasures. Despite exhibiting sparks of artificial general intelligence, LLMs are best suited for supporting roles rather than leading strategic decisions. Their use in training and wargaming can provide valuable insights and personalized learning experiences for military personnel, thereby improving operational readiness