The new Advanced Focusing Screen Display of the FinePix S3 Pro employs the convenient Vari-Brite Focus Area display system; it enables clear display of the focus brackets at the selected focus area in the viewfinder for easy identification. When the finder image is bright, the focus brackets are displayed in black and when the finder image is dark, the focus brackets are momentarily illuminated in red.
In recent years, a lot of effort has been dedicated to the theoretical and numerical treatment of models for fluid flow and deformation in porous media. Our model will be based on the classical phenomenological approach by Biot [2] using the concept of effective stresses. Modern formulations based on multiphase mixture theories were developed in the last 30 years, see the monograph by de Boer [12] for an overview. A lot of current research activity concerned with the numerical treatment of such coupled problems is devoted to the extension to two-phase flow and elasto-plastic deformation models.
The classical Biot consolidation problem assumes a fully saturated porous medium and a linearly elastic material law leading to a linear parabolic system. We restrict our attention to this linear problem since our aim is to analyze a least-squares mixed finite element method in the simplest possible situation. The novelty of our least-squares approach is the introduction of approximation spaces for the fluid flux and the stress tensor in addition to the primary variables fluid pressure and displacement field.
Rational expectations provides an elegant and powerful framework that has come to dominate thinking about the dynamic structure of the economy and econometric policy evaluation over the past 30 years. This success has spurred further examination into the strong information assumptions implicit in many of its applications. Thomas Sargent (1993) concludes that “rational expectations models impute much more knowledge to the agents within the model ... than is possessed by an econometrician, who faces estimation and inference problems that the agents in the model have somehow solved” (p. 3, emphasis in original).1 Researchers have proposed refinements to rational expectations that respect the principle that agents use information efficiently in forming expectations, but nonetheless recognize the limits to and costs of information-processing and cognitive constraints that influence the expectations-formation process (Sargent 1999, Evans and Honkapohja 2001, Sims 2003).
In this study, we allow for a form of imperfect knowledge in which economic agents rely on an adaptive learning technology to form expectations. This form of learning represents a relatively modest deviation from rational expectations that nests it as a limiting case. We show that the resulting process of perpetual learning introduces an additional layer of interaction between monetary policy and economic outcomes that has important implications for macroeconomic dynamics and for monetary policy design. As we illustrate, monetary policies that would be efficient under rational expectations can perform poorly when knowledge is imperfect. In particular, with imperfect knowledge, policies that fail to maintain tight control over inflation are prone to episodes in which the public’s expectations of inflation become uncoupled from the policy objective. The presence of this imperfection makes stabilization policy more difficult than would appear under rational expectations and highlights the value of effectively communicating a central bank’s inflation objective and of continued vigilance against inflation in anchoring inflation expectations and fostering macroeconomic stability.
