Inflation Targeting: to Forecast or to Simulate

Inflation targeting is a regime based to a great extent on communication and, more specifically, on using and communicating assessments of future inflation. The central banking literature, however, devotes surprisingly little attention to some important issues connected with such assessments. There are some non-trivial choices that need to be made regarding future inflation assessments on three distinct levels: construction, decision making and communication. One of the most important choices relates to the treatment of the central bank’s behaviour within the assessment. We first differentiate between two basic ways of assessing future inflation: forecast and simulation. A forecast is the most likely picture of the future. In a forecast, all agents are assumed to behave in the most likely way. A simulation, on the other hand, is the most likely picture of the future if the behaviour of one agent follows a predetermined path or is generated using a selected reaction function. The path or reaction function ascribed to the agent does not have to be the most likely one. After differentiating between a forecast and a simulation, we discuss the pros and cons of using the two ways of assessing future inflation on the three abovementioned levels.inflation targeting, forecast, simulation, central bank, decision making, communication

Inflation Targeting: To Forecast or to Simulate?

Inflation targeting is a regime based to a great extent on communication and, more specifically, on using and communicating assessments of future inflation. The central banking literature, however, devotes surprisingly little attention to some important issues connected with such assessments. There are some non-trivial choices that need to be made regarding future inflation assessments on three distinct levels: construction, decision making and communication. One of the most important choices relates to the treatment of the central bank's behaviour within the assessment. We first differentiate between two basic ways of assessing future inflation: forecast and simulation. A forecast is the most likely picture of the future. In a forecast, all agents are assumed to behave in the most likely way. A simulation, on the other hand, is the most likely picture of the future if the behaviour of one agent follows a predetermined path or is generated using a selected reaction function. The path or reaction function ascribed to the agent does not have to be the most likely one. After differentiating between a forecast and a simulation, we discuss the pros and cons of using the two ways of assessing future inflation on the three abovementioned levels.

Distributed formation control of multiple unmanned aerial vehicles over time-varying graphs using population games

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a control technique based on distributed population dynamics under time-varying communication graphs for a multi-agent system structured in a leader-follower fashion. Here, the leader agent follows a particular trajectory and the follower agents should track it in a certain organized formation manner. The tracking of the leader can be performed in the position coordinates x; y; and z, and in the yaw angle phi. Additional features are performed with this method: each agent has only partial knowledge of the position of other agents and not necessarily all agents should communicate to the leader. Moreover, it is possible to integrate a new agent into the formation (or for an agent to leave the formation task) in a dynamical manner. In addition, the formation configuration can be changed along the time, and the distributed population-games-based controller achieves the new organization goal accommodating conveniently the information-sharing graph in function of the communication range capabilities of each UAV. Finally, several simulations are presented to illustrate different scenarios, e.g., formation with time-varying communication network, and time-varying formationPeer ReviewedPostprint (author's final draft

Transferable Control

In this paper, we introduce the notion of transferable control, defined as a situation where one party (the principal, say) can transfer control to another party (the agent) but cannot commit herself to do so. One theoretical foundation for this notion builds on the distinction between formal and real authority introduced by Aghion and Tirole, in which the actual exercise of authority may require noncontractible information, absent which formal control rights are vacuous. We use this notion to study the extent to which control transfers may allow an agent to reveal information regarding his ability or willingness to cooperate with the principal in the future. We show that the distinction between contractible and transferable control can drastically influence how learning takes place: with contractible control, information about the agent can often be acquired through revelation mechanisms that involve communication and message-contingent control allocations; in contrast, when control is transferable but not contractible, it can be optimal to transfer control unconditionally and learn instead from the way in which the agent exercises control

Implicit Coordination in Two-Agent Team Problems; Application to Distributed Power Allocation

The central result of this paper is the analysis of an optimization problem which allows one to assess the limiting performance of a team of two agents who coordinate their actions. One agent is fully informed about the past and future realizations of a random state which affects the common payoff of the agents whereas the other agent has no knowledge about the state. The informed agent can exchange his knowledge with the other agent only through his actions. This result is applied to the problem of distributed power allocation in a two-transmitter $M-$band interference channel, $M\geq 1$, in which the transmitters (who are the agents) want to maximize the sum-rate under the single-user decoding assumption at the two receivers; in such a new setting, the random state is given by the global channel state and the sequence of power vectors used by the informed transmitter is a code which conveys information about the channel to the other transmitter.Comment: 6 pages, appears as WNC3 2014: International Workshop on Wireless Networks: Communication, Cooperation and Competition - International Workshop on Resource Allocation, Cooperation and Competition in Wireless Network

Toward an Agent-Agnostic Transmission Model: Synthesizing Anthropocentric and Technocentric Paradigms in Communication

Technological and social evolutions have prompted operational, phenomenological, and ontological shifts in communication processes. These shifts, we argue, trigger the need to regard human and machine roles in communication processes in a more egalitarian fashion. Integrating anthropocentric and technocentric perspectives on communication, we propose an agent-agnostic framework for human-machine communication. This framework rejects exclusive assignment of communicative roles (sender, message, channel, receiver) to traditionally held agents and instead focuses on evaluating agents according to their functions as a means for considering what roles are held in communication processes. As a first step in advancing this agent-agnostic perspective, this theoretical paper offers three potential criteria that both humans and machines could satisfy: agency, interactivity, and influence. Future research should extend our agent-agnostic framework to ensure that communication theory will be prepared to deal with an ostensibly machine-inclusive future