Money and Credit With Limited Commitment and Theft

We study the interplay among imperfect memory, limited commitment, and theft, in an environment that can support monetary exchange and credit. Imperfect memory makes money useful, but it also permits theft to go undetected, and therefore provides lucrative opportunities for thieves. Limited commitment constrains credit arrangements, and the constraints tend to tighten with imperfect memory, as this mitigates punishment for bad behavior in the credit market. Theft matters for optimal monetary policy, but at the optimum theft will not be observed in the model. The Friedman rule is in general not optimal with theft, and the optimal money growth rate tends to rise as the cost of theft falls.

Enter the Circle: Blending Spherical Displays and Playful Embedded Interaction in Public Spaces

Public displays are used a variety of contexts, from utility driven information displays to playful entertainment displays. Spherical displays offer new opportunities for interaction in public spaces, allowing users to face each other during interaction and explore content from a variety of angles and perspectives. This paper presents a playful installation that places a spherical display at the centre of a playful environment embedded with interactive elements. The installation, called Enter the Circle, involves eight chair-sized boxes filled with interactive lights that can be controlled by touching the spherical display. The boxes are placed in a ring around the display, and passers-by must “enter the circle” to explore and play with the installation. We evaluated this installation in a pedestrianized walkway for three hours over an evening, collecting on-screen logs and video data. This paper presents a novel evaluation of a spherical display in a public space, discusses an experimental design concept that blends displays with embedded interaction, and analyses real world interaction with the installation

Deep cover HCI: the ethics of covert research

No abstract available

Adverse Selection, Segmented Markets, and the Role of Monetary Policy

A model is constructed in which trading partners are asymmetrically informed about future trading opportunities and where spatial and informational frictions limit arbitrage between markets. These frictions create an inefficiency relative to a full information equilibrium, and the extent of this inefficiency is affected by monetary policy. A Friedman rule is optimal under a wide range of circumstances, including ones where segmented markets limit the extent of monetary policy intervention.Adverse Selection; Monetary Policy; Search

Money and Credit With Limited Commitment and Theft

We study the interplay among imperfect memory, limited commitment, and theft, in an environment that can support monetary exchange and credit. Imperfect memory makes money useful, but it also permits theft to go undetected, and therefore provides lucrative opportunities for thieves. Limited commitment constrains credit arrangements, and the constraints tend to tighten with imperfect memory, as this mitigates punishment for bad behavior in the credit market. Theft matters for optimal monetary policy, but at the optimum theft will not be observed in the model. The Friedman rule is in general not optimal with theft, and the optimal money growth rate tends to rise as the cost of theft falls.Money; Credit; Limited Commitment; Monetary Policy

How are emergent constraints quantifying uncertainty and what do they leave behind?

The use of emergent constraints to quantify uncertainty for key policy relevant quantities such as Equilibrium Climate Sensitivity (ECS) has become increasingly widespread in recent years. Many researchers, however, claim that emergent constraints are inappropriate or even under-report uncertainty. In this paper we contribute to this discussion by examining the emergent constraints methodology in terms of its underpinning statistical assumptions. We argue that the existing frameworks are based on indefensible assumptions, then show how weakening them leads to a more transparent Bayesian framework wherein hitherto ignored sources of uncertainty, such as how reality might differ from models, can be quantified. We present a guided framework for the quantification of additional uncertainties that is linked to the confidence we can have in the underpinning physical arguments for using linear constraints. We provide a software tool for implementing our general framework for emergent constraints and use it to illustrate the framework on a number of recent emergent constraints for ECS. We find that the robustness of any constraint to additional uncertainties depends strongly on the confidence we can have in the underpinning physics, allowing a future framing of the debate over the validity of a particular constraint around the underlying physical arguments, rather than statistical assumptions