Quantitative Verification: Formal Guarantees for Timeliness, Reliability and Performance

Computerised systems appear in almost all aspects of our daily lives, often in safety-critical scenarios such as embedded control systems in cars and aircraft or medical devices such as pacemakers and sensors. We are thus increasingly reliant on these systems working correctly, despite often operating in unpredictable or unreliable environments. Designers of such devices need ways to guarantee that they will operate in a reliable and efficient manner. Quantitative verification is a technique for analysing quantitative aspects of a system's design, such as timeliness, reliability or performance. It applies formal methods, based on a rigorous analysis of a mathematical model of the system, to automatically prove certain precisely specified properties, e.g. ``the airbag will always deploy within 20 milliseconds after a crash'' or ``the probability of both sensors failing simultaneously is less than 0.001''. The ability to formally guarantee quantitative properties of this kind is beneficial across a wide range of application domains. For example, in safety-critical systems, it may be essential to establish credible bounds on the probability with which certain failures or combinations of failures can occur. In embedded control systems, it is often important to comply with strict constraints on timing or resources. More generally, being able to derive guarantees on precisely specified levels of performance or efficiency is a valuable tool in the design of, for example, wireless networking protocols, robotic systems or power management algorithms, to name but a few. This report gives a short introduction to quantitative verification, focusing in particular on a widely used technique called model checking, and its generalisation to the analysis of quantitative aspects of a system such as timing, probabilistic behaviour or resource usage. The intended audience is industrial designers and developers of systems such as those highlighted above who could benefit from the application of quantitative verification,but lack expertise in formal verification or modelling

The disposition of failed Japanese bank assets: lessons from the U.S. savings and loan crisis

This paper reviews the Japanese experience with “put guarantees” recently offered in the sale of several failed banks. These guarantees, meant to address information asymmetry problems, are shown to create moral hazard problems of their own. In particular, the guarantees make acquiring banks reluctant to accept first-best renegotiations with problem borrowers. These issues also arose in the U.S. Savings and Loan crisis. Regulators in that crisis turned to an alternative guarantee mechanism known as “loss-sharing arrangements,” with apparently positive results. I introduce a formal debt model to examine the conditions determining the relative merits of these guarantees. The results show that both forms of guarantees reduce expected regulator revenues, but that the impact of economic downturns on the relative desirability of the two guarantees is ambiguous. ; Published in FRBSF Economic Review (2002), p 1-15Japan

Collateral and its Substitutes in Emerging Markets' Lending

Due to opaque information and weak enforcement in emerging loan markets, the need for collateral is high, whereas borrowers lack adequate assets to pledge as collateral. How is this puzzle solved? We find for a representative sample from Northeast Thailand that indeed most loans do not include any tangible assets as collateral. Instead, lenders enforce collateral-free loans through third-party guarantees and relationship lending, but also through modifying loan terms, such as reducing loan size. Guarantees are the relatively most important substitute, they reduce collateral requirements independently of relationship lending and they are more often used by formal financial institutions.lending, financial institutions, collateral, guarantees, relationship lending

Decentralized Learning for Optimality in Stochastic Dynamic Teams and Games with Local Control and Global State Information

Stochastic dynamic teams and games are rich models for decentralized systems and challenging testing grounds for multi-agent learning. Previous work that guaranteed team optimality assumed stateless dynamics, or an explicit coordination mechanism, or joint-control sharing. In this paper, we present an algorithm with guarantees of convergence to team optimal policies in teams and common interest games. The algorithm is a two-timescale method that uses a variant of Q-learning on the finer timescale to perform policy evaluation while exploring the policy space on the coarser timescale. Agents following this algorithm are "independent learners": they use only local controls, local cost realizations, and global state information, without access to controls of other agents. The results presented here are the first, to our knowledge, to give formal guarantees of convergence to team optimality using independent learners in stochastic dynamic teams and common interest games