An Investigation of the Gains from Commitment in Monetary Policy

This paper proposes a simple framework for analyzing a continuum of monetary policy rules characterized by differing degrees of credibility, in which commitment and discretion become special cases of what we call quasi commitment. The monetary policy authority is assumed to formulate optimal commitment plans, to be tempted to renege on them, and to succumb to this temptation with a constant exogenous probability known to the private sector. By interpreting this probability as a continuous measure of the (lack of) credibility of the monetary policy authority, we investigate the welfare effect of a marginal increase in credibility. Our main finding is that, in a simple model of the monetary transmission mechanism, most of the gains from commitment accrue at relatively low levels of credibility. In our benchmark calibration, a commitment expected to last for only 6 quarters is enough to bridge 75% of the welfare gap between discretion and commitment. This seems to justify the well known concern of monetary policy makers about their credibility, even in a world with limited access to commitment technologiesCommitment, discretion, credibility, welfare

An Investigation of the Gains from Commitment in Monetary Policy

This paper proposes a simple framework for analyzing a continuum of monetary policy rules characterized by differing degrees of credibility, in which commitment and discretion become special cases of what we call quasi commitment. The monetary policy authority is assumed to formulate optimal commitment plans, to be tempted to renege on them, and to succumb to this temptation with a constant exogenous probability known to the private sector. By interpreting this probability as a continuous measure of the (lack of) credibility of the monetary policy authority, we investigate the welfare effect of a marginal increase in credibility. Our main finding is that, in a simple model of the monetary transmission mechanism, most of the gains from commitment accrue at relatively low levels of credibility. In our benchmark calibration, a commitment expected to last for only 6 quarters is enough to bridge 75% of the welfare gap between discretion and commitment. This seems to justify the well known concern of monetary policy makers about their credibility, even in a world with limited access to commitment technologies.Commitment, discretion, credibility, welfare

Multi-agent Reinforcement Learning as Applied to Autonomous Systems

Multi-agent reinforcement learning (MARL) is a relatively unexplored area. Existing MARL methods and algorithms often scale poorly to the number of agents or suffer from the issue of non-stationary learning. This thesis aims to develop distributed training methods and algorithms for RL of the multi-agent autonomous systems that ensure scalability and stabilized learning.Specifically, we consider three common paradigms of multi-agent interaction: cooperative/ team, strategic/competitive, and leader-follower settings. We first study game-theoretic RL for the last two settings. Game-theoretic models are inherently distributed, and each agent takes other agents’ responses into account in the resulting game-theoretical equilibrium. Therefore, learning based on the game-theoretic equilibrium can effectively address the issue of non-stationary learning. We investigate Nash Q-learning in the strategic/competitive setting and Stackelberg Q-learning in the leader-follower setting, and apply them successfully to several applications that involve simple yet basic multi-agent interactions. We then propose a distributed deep Q-leaning for the cooperative/team setting, where each agent updates the estimate of her Q-value based on her own reward and her neighbors’ Q-values. We analyze the convergence of the proposed algorithm, characterize its performance gap to the centralized Q-learning, and evaluate it with a cooperative multi-agent navigation task

Managing Beliefs about Monetary Policy under Discretion?

Optimal monetary policy becomes tricky when the central bank has better information than the public: Policy does not only affect economic fundamentals, but also people’s beliefs. For a general class of widely studied DSGE models, this paper derives the optimal discretionary policy under hidden information. Illustrated with a simple New Keynesian model, the introduction of hidden information has striking effects on discretionary policies: Policy losses are better under hidden information than under full transparency. Looking at Markov-perfect policies excludes reputational mechanisms via history dependent strategies. Under full transparency, discretion policies are then myopic, since a current policymaker cannot influence future decisions. But imperfect information adds public beliefs as a distinct, endogenous state variable. Managing beliefs connects the actions of policymakers such that they realize the inflationary consequences of expansionary policies. The optimal policy shares similarities with those from commitment models. Additionally, disinflations are pursued more vigorously the larger the credibility problems from hidden information. Optimal policy also responds to belief shocks, which shift public perceptions about fundamentals even when those fundamentals are unchanged.

Operational Decision Making under Uncertainty: Inferential, Sequential, and Adversarial Approaches

Modern security threats are characterized by a stochastic, dynamic, partially observable, and ambiguous operational environment. This dissertation addresses such complex security threats using operations research techniques for decision making under uncertainty in operations planning, analysis, and assessment. First, this research develops a new method for robust queue inference with partially observable, stochastic arrival and departure times, motivated by cybersecurity and terrorism applications. In the dynamic setting, this work develops a new variant of Markov decision processes and an algorithm for robust information collection in dynamic, partially observable and ambiguous environments, with an application to a cybersecurity detection problem. In the adversarial setting, this work presents a new application of counterfactual regret minimization and robust optimization to a multi-domain cyber and air defense problem in a partially observable environment

Poverty Traps

no abstract given.Poverty