286,772 research outputs found
Two-stage Boundedly Rational Choice Procedures: Theory and Experimental Evidence
We study and test a class of boundedly rational models of decision making which rely on sequential eliminative heuristics. We formalize two sequential decision procedures, both inspired by plausible models popular among several psychologists and marketing scientists. However we follow a standard `revealed preference' economic approach by fully characterizing these procedures by few, simple and testable conditions on observed choice. Then we test the models (as well as the standard utility maximization model) with experimental data. We find that the large majority of individuals behave in a way consistent with one of our procedures, and inconsistent with the utility maximization model.Bounded rationality, Choice experiments
Sequential Estimation of Structural Models with a Fixed Point Constraint
This paper considers the estimation problem of structural models for which empirical restrictions are characterized by a fixed point constraint, such as structural dynamic discrete choice models or models of dynamic games. We analyze the conditions under which the nested pseudo-likelihood (NPL) algorithm converges to a consistent estimator and derive its convergence rate. We find that the NPL algorithm may not necessarily converge to a consistent estimator when the fixed point mapping does not have a local contraction property. To address the issue of divergence, we propose alternative sequential estimation procedures that can converge to a consistent estimator even when the NPL algorithm does not.contraction, dynamic games, nested pseudo likelihood, recursive projection method
Sequential Estimation of Structural Models with a Fixed Point Constraint
This paper considers the estimation problem of structural models for which empirical restrictions are characterized by a fixed point constraint, such as structural dynamic discrete choice models or models of dynamic games. We analyze the conditions under which the nested pseudo-likelihood (NPL) algorithm achieves convergence and derive its convergence rate. We find that the NPL algorithm may not necessarily converge when the fixed point mapping does not have a local contraction property. To address the issue of non-convergence, we propose alternative sequential estimation procedures that can achieve convergence even when the NPL algorithm does not. Upon convergence, some of our proposed estimation algorithms produce more efficient estimators than the NPL estimator.contraction, dynamic games, nested pseudo likelihood, recursive projection method
The Choice Function Framework for Online Policy Improvement
There are notable examples of online search improving over hand-coded or
learned policies (e.g. AlphaZero) for sequential decision making. It is not
clear, however, whether or not policy improvement is guaranteed for many of
these approaches, even when given a perfect evaluation function and transition
model. Indeed, simple counter examples show that seemingly reasonable online
search procedures can hurt performance compared to the original policy. To
address this issue, we introduce the choice function framework for analyzing
online search procedures for policy improvement. A choice function specifies
the actions to be considered at every node of a search tree, with all other
actions being pruned. Our main contribution is to give sufficient conditions
for stationary and non-stationary choice functions to guarantee that the value
achieved by online search is no worse than the original policy. In addition, we
describe a general parametric class of choice functions that satisfy those
conditions and present an illustrative use case of the framework's empirical
utility
Sequential Estimation of Structural Models with a Fixed Point Constraint
This paper considers the estimation problem of structural models for which empirical restrictions are characterized by a fixed point constraint, such as structural dynamic discrete choice models or models of dynamic games. We analyze the conditions under which the nested pseudo-likelihood (NPL) algorithm achieves convergence and derive its convergence rate. We find that the NPL algorithm may not necessarily converge when the fixed point mapping does not have a local contraction property. To address the issue of non-convergence, we propose alternative sequential estimation procedures that can achieve convergence even when the NPL algorithm does not. Upon convergence, some of our proposed estimation algorithms produce more efficient estimators than the NPL estimator.contraction, dynamic games, nested pseudo likelihood, recursive projection method
Adaptive optimal estimation control strategies for systems of simultaneous equations
AbstractThe choice of control strategies to improve estimation of the parameters in a model of a simultaneous equations system with time-varying parameters is considered. Open-loop feedback (OLF) sequential procedures for handling nonlinear restrictions on reduced form parameters implied by the structural form are suggested, and the combination of sequential estimation and design control strategies feature a marked improvement in the behavior of estimates over the nonsequential [open-loop (OL)] formulation. The maximum accuracy control problem considered in this paper can also be treated as an initial phase of a forecasting and/or stochastic control problem. This will avoid solution to a more difficult problem such as the dual control problem
The effect of lineup member similarity on recognition accuracy in simultaneous and sequential lineups.
Two experiments investigated whether remembering is affected by the similarity of the study face relative to the alternatives in a lineup. In simultaneous and sequential lineups, choice rates and false alarms were larger in low compared to high similarity lineups, indicating criterion placement was affected by lineup similarity structure (Experiment 1). In Experiment 2, foil
choices and similarity ranking data for target present lineups were compared to responses made when the target was removed from the lineup (only the 5 foils were presented). The results indicated that although foils were selected more often in target-removed lineups in the simultaneous compared to the sequential condition, responses shifted from the target to one of
the foils at equal rates across lineup procedures
A compiler approach to scalable concurrent program design
The programmer's most powerful tool for controlling complexity in program design is abstraction. We seek to use abstraction in the design of concurrent programs, so as to
separate design decisions concerned with decomposition, communication, synchronization, mapping, granularity, and load balancing. This paper describes programming and compiler techniques intended to facilitate this design strategy. The programming techniques are based on a core programming notation with two important properties: the ability to separate concurrent programming concerns, and extensibility with reusable programmer-defined
abstractions. The compiler techniques are based on a simple transformation system together with a set of compilation transformations and portable run-time support. The
transformation system allows programmer-defined abstractions to be defined as source-to-source transformations that convert abstractions into the core notation. The same
transformation system is used to apply compilation transformations that incrementally transform the core notation toward an abstract concurrent machine. This machine can be implemented on a variety of concurrent architectures using simple run-time support.
The transformation, compilation, and run-time system techniques have been implemented and are incorporated in a public-domain program development toolkit. This
toolkit operates on a wide variety of networked workstations, multicomputers, and shared-memory
multiprocessors. It includes a program transformer, concurrent compiler, syntax checker, debugger, performance analyzer, and execution animator. A variety of substantial
applications have been developed using the toolkit, in areas such as climate modeling and fluid dynamics
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